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REVIEW 5 major objections 6 minor 1 cited by

Realistic Surgical Simulation from Monocular Videos

T0 review · 5 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read SurgiSim claims a single monocular surgical video can become a physically simulated soft-tissue scene, with stiffness and viscosity inferred from the footage itself.

desk verdict SurgiSim is a genuine end-to-end engineering integration that works in practice, but the realism claim is undermined by a circular evaluation and a wrong equation in a stated contribution. read the letter →

arxiv 2412.02359 v1 pith:5OZAFDCK submitted 2024-12-03 cs.CV

Kailing Wang , Chen Yang , Keyang Zhao , Xiaokang Yang , Wei Shen This is my paper · ORCID
classification cs.CV
keywords 3DGaussianSplattingsurgicalsimulationMaterialPointMethodviscoelasticitymonocularvideoreconstructionphysicalparameterestimationsofttissuedeformationdeformablescene
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that a single monocular surgical video is enough to build a simulation-ready, physically behaving soft-tissue scene. SurgiSim reconstructs a canonical 3D Gaussian model with a deformation field, using trajectory and anisotropy regularizations so the geometry stays consistent over time, and thickens the surface to give the simulator a volume to deform. It then fits tissue stiffness and viscosity by comparing rendered simulations against the observed video, guided by 3D trajectories obtained from point tracking and monocular depth. If the claim holds, realistic surgical simulators could be produced automatically from routine recordings, without hand-built meshes or manually tuned physics parameters.

What carries the argument

The load-bearing pieces are (1) a canonical 3D Gaussian Splatting scene coupled to a multi-resolution feature-plane and MLP deformation field, trained in stages with trajectory regularization (deformations in a small neighborhood stay parallel and small) and anisotropic regularization (Gaussians stay compact) to keep the canonical geometry physically consistent; (2) a surface-thickening step that turns the reconstructed shell into a volumetric particle set; (3) a Material Point Method simulator with a visco-elastic stress combining fixed corotated elasticity and a Maxwell-type viscous dissipation potential; and (4) automatic physics-parameter estimation in which 2D dense point tracks near the tool contact, lifted by monocular depth into 3D trajectories, are injected as velocity boundary conditions in the MPM update, and stiffness, viscosity, and damping are optimized by rasterizing the simulated Gaussians and minimizing the difference from the input video frames.

What would settle it

Record a surgical scene with a calibrated stereo rig and a tracked tool applying a known force, run SurgiSim's estimation, and compare the inferred Young's modulus and viscosity with independently measured values or with the observed rebound time; if simulated and real displacement profiles diverge under the same manipulation, the trajectory-lifting step is the prime suspect.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a monocular surgical video contains enough information to build both a simulation-ready scene and the tissue's material response. The system reconstructs a canonical 3D Gaussian scene whose deformations over time are explained by a deformation field, regularized so that neighboring Gaussian trajectories stay parallel and the kernels stay compact; it thickens this shell into a volume; and it then fits a visco-elastic material model (fixed corotated elasticity plus a Maxwell-style viscous dissipation) by lifting dense 2D point tracks near the tool into 3D trajectories with monocular depth, injecting those as velocity boundary conditions in the material-point simulation, and minimizing the difference between rendered simulation frames and the input video. The result is a scene that reproduces the recorded manipulation and can respond to new tool interactions with damped, tissue-like motion rather than the unbounded elastic oscillations of the baselines.

Load-bearing premise

The inferred tissue properties are only as trustworthy as the 3D trajectories, which are built from single-camera depth estimates and 2D point tracks; any depth-scale error or tracking drift directly biases the estimated stiffness and viscosity, and therefore the simulated tissue response.

Editorial extensions

If this is right

  • Surgical training simulators could be generated automatically from archived operation videos, removing the manual mesh-modeling step.
  • A robotic surgery system could rehearse a planned manipulation on the reconstructed tissue and observe the predicted deformation before acting.
  • Because stiffness, viscosity, and damping are fit per video, the simulation inherits the specific tissue response of the recorded procedure instead of generic preset values.
  • Under the paper's visco-elastic model, released tissue quickly returns to rest with brief damping, matching observed surgical behavior where elastic-only baselines keep oscillating.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves implicit that, if inferred parameters transfer across clips of the same tissue type, the method could accumulate a per-procedure library of tissue material models; that is a natural next step the authors do not pursue.
  • A testable extension would be calibrating the monocular depth scale against a known instrument dimension in the frame; the paper's estimated stiffness and viscosity inherit whatever scale error the depth model has, so a calibration check would show whether absolute material values are recoverable.
  • The paper's own future-work note concedes that invisible side textures and cut surfaces still need manual correction and that topological inversions are unsupported, so the demonstrated interactions are limited to pulling and compressing rather than tearing tissue.
  • The parameter-estimation loop currently drives motion from prescribed trajectories; coupling the simulator with a rigid tool model so forces emerge from contact would let the system generalize to tool actions not present in the input video.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. SurgiSim proposes an automatic pipeline that takes a monocular surgical video as input, reconstructs a canonical 3D Gaussian scene with a deformation field using multi-stage optimization with trajectory and anisotropic regularization plus surface thickening, and then runs MPM-based soft-tissue simulation with a proposed viscoelastic model. The physical parameters are inferred by minimizing the discrepancy between simulated renderings and the input video, with tissue motion obtained by lifting 2D dense point tracks using per-frame monocular depth estimates. Experiments on EndoNeRF clips report simulation-reproduction metrics, a user study with surgeons and laypersons, and ablations of the reconstruction and parameter-estimation components.

Significance. The end-to-end goal is valuable: if the reconstruction and physics are sound, SurgiSim would reduce manual mesh building and parameter tuning for surgical simulation, and the attempt to infer constitutive parameters from video through differentiable MPM and rasterization is a useful research direction. The user study, the automatic pipeline, and the qualitative demonstration of pulling and cutting interactions are clear strengths. However, several load-bearing technical points need correction or additional validation before the central claims of geometric consistency and realistic viscoelastic tissue dynamics can be accepted.

major comments (5)
  1. [§3.2, Eq. (7)] The trajectory regularizer is algebraically L_traj = Σ_{Gj,Gk∈Ni} Δµ_j · Δµ_k, because the norm factors in the numerator and denominator cancel. As written, minimizing this objective encourages displacement pairs to be anti-parallel rather than parallel, which is the opposite of the stated goal that the deformation directions should be parallel. The second stated goal, that movement length should be small, is also incompatible with a raw dot-product term, whose magnitude grows with displacement length. Please correct the loss (for example, use negative cosine similarity with a separate length penalty) and rerun the affected experiments; the current Table 3 does not contain a geometry metric and therefore cannot establish the claimed geometric-consistency benefit.
  2. [§3.3, Eqs. (11)–(12)] The viscous model is not consistently derived from the stated dissipation potential. Differentiating Ψ(∂F_v/∂t) = 1/2 η_v tr((∂F_v/∂t)^T ∂F_v/∂t) gives a stress proportional to η_v ∂F_v/∂t, not σ_v = det(F_v)·2η_vD, and the relation ∂F_v/∂t = γD introduces parameters that do not appear in a single constitutive equation: γ_v appears in the update of F_v while η_v appears in the stress. The paper calls the model a Maxwell model, but the additive stress σ = σ_E + σ_v corresponds to a Kelvin–Voigt (parallel) arrangement rather than a Maxwell (series) arrangement. Please provide a consistent finite-strain viscoelastic formulation and clarify which rheological element is actually intended.
  3. [§3.3 and Table 1] The physical parameters are optimized by minimizing Eq. (14) against the input video, and Table 1 evaluates the simulation by reproducing the operation in the same input videos. This is an in-sample fitting test, not a test of predictive realism: errors in depth scale, tracking drift, and model misspecification can be absorbed into the fitted µE, ηv, and γv while still matching the training frames. To support the claim of realistic simulation under new interactions, the paper needs held-out validation, such as simulating an interaction not used for fitting, or comparing the predicted free rebound against frames not included in Eq. (14), together with a report of the fitted parameter ranges.
  4. [§3.3, Eq. (13)] The 3D trajectories {p_t} are obtained by lifting 2D dense tracks with per-frame Depth Anything v2 estimates, and the paper does not describe any alignment of depth scale or shift across frames. Monocular depth is defined only up to per-image scale and shift, so the time differences in Eq. (13) mix apparent depth-scale drift with true tissue motion, and any error directly biases the injected MPM velocities and the inferred constitutive parameters. Because EndoNeRF is a stereo dataset, the authors could validate the monocular trajectories against stereo-derived depths (as they do in the supplementary reconstruction comparison of Table 4), or add a scale-calibration step; without this, the primary physical supervision is unvalidated.
  5. [§4.3, Table 3] The ablation reports that removing trajectory regularization changes PSNR, SSIM, and LPIPS only slightly (e.g., PSNR 37.114 vs. 37.036), and the paper argues that this regularizer affects geometry rather than rendering quality. However, no geometric metric is provided, such as point-to-surface distance, normal consistency, or a direct measure of trajectory parallelism. Without such a metric, the central claim that trajectory regularization improves the geometric consistency of the simulation environment is not quantitatively supported, especially given the algebraic issue in Eq. (7).
minor comments (6)
  1. [Algorithm 1] The text says the surface thickening method pushes Gaussian kernels along the z-axis, but line 6 of Algorithm 1 scales all position coordinates by (rand(3)+l)/1000; please clarify the intended transformation and correct the inconsistency.
  2. [§3.3] The sentence "These corrupted definitions are only used in this section, and their meaning will be re-defined" appears to be a typo and should read "redefined"; the current wording is confusing.
  3. [Table 2] The table header and row labels for the two-alternative forced-choice comparison ("Ours w.o. Guide") are ambiguous; please present the four-way comparison and the paired comparison as separate blocks with clear column headings.
  4. [References] Some citations appear mismatched: reference [42] is NR-SLAM rather than a tissue-mechanics or viscoelasticity reference, and references [14] and [15] in the MPM related-work sentence appear to be unrelated medical and chemistry papers; these should be corrected.
  5. [Table 4 and §4.2] The supplementary reconstruction comparison uses stereo depth and the original dataset masks, whereas the main simulation pipeline uses monocular depth and SAM-refined masks; please state explicitly that Table 4 does not validate the monocular simulation setup.
  6. [Header and formatting] The line "Published at Top Journal Arxiv" and the superscript "B" after the last author appear to be formatting artifacts and should be removed.

Circularity Check

2 steps flagged · score 6.0 of 10

The main quantitative simulation evaluation is in-sample: physical parameters are fit to the input video (Eq. 14) and then scored by reproducing that same video (Table 1), so the reported reproduction is a fit; user-study and novel-operation results provide some independent signal.

  1. fitted input called prediction [Sec. 3.3, Eq. (14); Sec. 4.2, Table 1]
    "We then use the input video to estimate the physical parameters of tissues. This estimation is achieved by minimizing the discrepancies between frames from the input video and the simulation results ... Lv = ∥Ct o − Ct s∥1 · Mt. (14) ... We reproduce the operation in the input videos and compare the render results with the ground truth."

    The parameters µE, ηv, and γv are optimized by Eq. (14) to minimize the difference between the rendered simulation and the input video. The paper's main quantitative simulation evaluation then re-runs the same operation from the same input video and scores PSNR/SSIM/LPIPS against that video. This is an in-sample fit: the optimizer is explicitly trained to match those frames, so agreement with them is forced by the objective and cannot validate the estimated constitutive parameters. Appearance overfitting of the renderer would improve Table 1 without implying correct physics.

  2. self definitional [Sec. 3.3, Eq. (13) and Eq. (14)]
    "We start by selecting pixels on the tissues near the contact point with the surgical instruments and employ a 2D dense optical tracking method (25) to capture the 2D trajectories of tissue movements directly influenced by the external forces. We then augment these 2D trajectories with estimated depth values, { ˆDt}T t=1, to derive 3D trajectories, {pt}T t=1. ... The velocity is updated according to the following formula: vt B0 = pnt+1 − pnt / ∆T , (13)"

    The simulated motion is not generated solely by the physics model: the video's own recovered trajectory is injected as a velocity boundary condition (Eq. 13), and the same video is then used as the fitting target (Eq. 14) and as the evaluation ground truth (Table 1). The output therefore inherits the input motion by construction; the reported 'reproduction quality' measures how well the renderer follows the video's motion under the fitted parameters, not whether the visco-elastic model predicts unobserved tissue behavior. The independent component is limited to post-release rebound and user preference for new operations.

full rationale

The reconstruction part of the paper is largely self-contained: canonical 3D Gaussians with a deformation field, trajectory regularization, anisotropic regularization, and surface thickening are standard techniques, and the reconstruction comparison in Table 4 is run under the dataset's stereo-depth protocol rather than the monocular pipeline. I found no load-bearing self-citation chain or imported 'uniqueness theorem.' The circularity is concentrated in the physical-parameter evaluation. Eq. (14) fits µE, ηv, and γv by minimizing the discrepancy between simulation renders and the input video; Table 1 then 'reproduces the operation in the input videos' and reports quality metrics against that same video. This makes the headline quantitative result a training-fit comparison rather than an independent prediction of tissue behavior. The user study (Table 2) and additional simulation clips with different operations do provide some independent, though subjective, signal, and they partially support the claim that the estimated parameters improve realism. The monocular depth-scale and tracking-drift issue is a real correctness risk but is not itself circularity. Overall, the central simulation-quality evaluation reduces partly to the fitting objective, giving a partial-circularity score of 6.

Assumptions & free parameters 9 free parameters · 7 assumptions · 0 invented entities

The system rests on strong prior literature (3DGS, MPM, SAM, Depth Anything, dense tracking) plus several unvalidated modeling choices. The most consequential are the representation of tissue as a canonical Gaussian field, the ad-hoc Maxwell-type viscosity law, the treatment of tracked 2D plus monocular-depth trajectories as ground-truth motion, and the hand-set simulation time scaling and thickening rules. The physical parameters are fitted per scene to the input video rather than measured or cross-validated, so they contribute little independent evidence.

free parameters (9)
  • Shear modulus µE (spatially varying) = Not reported; optimized per scene
    Elastic stiffness is optimized by minimizing Eq. 14 against the input video; the numeric values are not disclosed for any scene.
  • Viscosity coefficient ηv (spatially varying) = Not reported; optimized per scene
    Viscous damping coefficient is optimized in the same parameter estimation loop; no fitted values or ranges are given.
  • Viscous flow factor γv (spatially varying) = Not reported; optimized per scene
    Scales the strain-rate term in Eq. 12 and is optimized together with µE and ηv; values are not reported.
  • Poisson ratio νE = 0.45
    Fixed by hand in Sec. 3.3 with the statement that variations have little impact; it still sets the elastic response.
  • Regularization thresholds r_m and r_ani = r_m=1, r_ani=3
    Hand-chosen limits in Eq. 8 that control maximum Gaussian scale and anisotropy.
  • Simulation time scaling = 10,000 sim steps per second; 80k steps per 8 s
    Hand-set conversion between MPM steps and video time, affecting all simulated dynamics.
  • Surface thickening hyperparameters = 1000 layers, 25% z extension, random scaling factor
    Algorithm 1 adds artificial volume to the reconstructed surface; the layer count, z-bound 1.25 z_M, and random scaling are chosen without independent support and directly shape the simulated material.
  • Trajectory and TV neighborhood size k = Not reported
    The k-nearest neighbor sets in Eq. 7 and Eq. 15 are central to both regularizers, but the paper never reports the value of k.
  • MPM velocity injection region B0 = Not specified
    Eq. 13 modifies grid velocities only in a small region B0 around p0; its size and shape control how the tracked trajectory is transferred to the grid but are not specified.
assumptions (7)
  • domain assumption Canonical 3D Gaussian scene plus a compact deformation field can represent deformable tissue across time.
    The entire simulation environment is defined as a canonical Gaussian model deformed by an MLP or feature-plane field; this representation is assumed expressive enough for surgical scenes.
  • domain assumption Monocular depth from Depth Anything v2 and 2D tracking from dense optical tracking yield 3D tissue trajectories consistent with the Gaussian scene scale.
    The 3D trajectories p_t are formed by lifting 2D tracks with estimated depth and are then imposed as MPM boundary velocities in Eq. 13. Any scale or drift error biases the inferred parameters.
  • ad hoc to paper A Maxwell-type viscoelastic model with total stress sigma = sigma_E + sigma_v, where sigma_v = det(F_v)*2*eta*D, captures the tissue behaviors relevant to surgery.
    The paper states it proposes a simple model and gets inspiration from Simo and Miehe and Johnson and Quigley, but does not validate the model against measured tissue rheology or derive sigma_v from the dissipation potential.
  • domain assumption Gravity is negligible in the operating scenarios.
    The paper states Gravity is ignored in Sec. 3.1. Ignoring gravity simplifies the momentum equation and may bias oscillation frequency and rest shape for freely moving tissue.
  • ad hoc to paper SAM masks, video inpainting, and the surface thickening algorithm produce a plausible complete tissue surface.
    The reconstructed canonical model is a front-view surface; extra Gaussian layers are synthesized by random scaling to create volume. The paper's own future-work section admits texture imperfections on invisible and cut regions.
  • domain assumption Spatially varying physical parameters with total-variation smoothing are sufficient, and fixing νE at 0.45 is harmless.
    The model assumes local homogeneity within neighbor groups and that varying Poisson ratio has negligible effect; no sensitivity analysis is provided.
  • domain assumption Optimizing photometric loss on the first 25 frames plus regularizers produces a geometrically consistent canonical model.
    The paper's geometry evaluation is indirect through rendering metrics and ablations, and the ablation shows trajectory regularization has little effect on PSNR or SSIM.

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Cite this review

Pith. "Pith review of Realistic Surgical Simulation from Monocular Videos." pith.science (2026). https://pith.science/paper/5OZAFDCK

@misc{pith2026241202359,
  author       = {Pith},
  title        = {Pith review of: Realistic Surgical Simulation from Monocular Videos},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5OZAFDCK}},
  note         = {Machine review of arXiv:2412.02359}
}
read the original abstract

This paper tackles the challenge of automatically performing realistic surgical simulations from readily available surgical videos. Recent efforts have successfully integrated physically grounded dynamics within 3D Gaussians to perform high-fidelity simulations in well-reconstructed simulation environments from static scenes. However, they struggle with the geometric inconsistency in reconstructing simulation environments and unrealistic physical deformations in simulations of soft tissues when it comes to dynamic and complex surgical processes. In this paper, we propose SurgiSim, a novel automatic simulation system to overcome these limitations. To build a surgical simulation environment, we maintain a canonical 3D scene composed of 3D Gaussians coupled with a deformation field to represent a dynamic surgical scene. This process involves a multi-stage optimization with trajectory and anisotropic regularization, enhancing the geometry consistency of the canonical scene, which serves as the simulation environment. To achieve realistic physical simulations in this environment, we implement a Visco-Elastic deformation model based on the Maxwell model, effectively restoring the complex deformations of tissues. Additionally, we infer the physical parameters of tissues by minimizing the discrepancies between the input video and simulation results guided by estimated tissue motion, ensuring realistic simulation outcomes. Experiments on various surgical scenarios and interactions demonstrate SurgiSim's ability to perform realistic simulation of soft tissues among surgical procedures, showing its enormous potential for enhancing surgical training, planning, and robotic surgery systems. The project page is at https://namaenashibot.github.io/SurgiSim/.

Figures

Figures reproduced from arXiv: 2412.02359 by the authors.

Figure 1
Figure 1. An overview of SurgiSim. From an input surgical video, we first reconstruct a 3D scene as the simula [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustration of our physical parameter estimation. SurgiSim automatically infers physical parameters by [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Visualization of simulations. We show the trajectory direction with a blue arrow and the motion of the [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Y-T slices of simulation dynamics. The slices at [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Ablation on the trajectory and geometric regulation. (a) is the input video as a reference. (b) is the [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Visualization of more simulation cases on different scenes using SurgiSim. [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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Reference graph

Works this paper leans on

70 extracted references · 49 canonical work pages · cited by 1 Pith paper

  1. [1]

    4d visualization of dynamic events from unconstrained multi-view videos

    Aayush Bansal, Minh V o, Yaser Sheikh, Deva Ramanan, and Srinivasa Narasimhan. 4d visualization of dynamic events from unconstrained multi-view videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5366–5375, 2020

    work page 2020

  2. [2]

    Stable video diffusion: Scaling latent video diffusion models to large datasets

    Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram V oleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127, 2023

    arXiv 2023

  3. [3]

    Nonlinear continuum mechanics for finite element analysis

    Javier Bonet and Richard D Wood. Nonlinear continuum mechanics for finite element analysis. Cambridge university press, 1997

    1997

  4. [4]

    Hexplane: A fast representation for dynamic scenes

    Ang Cao and Justin Johnson. Hexplane: A fast representation for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 130–141, 2023

    work page 2023

  5. [5]

    Sur- gen: Text-guided diffusion model for surgical video generation

    Joseph Cho, Samuel Schmidgall, Cyril Zakka, Mrudang Mathur, Rohan Shad, and William Hiesinger. Sur- gen: Text-guided diffusion model for surgical video generation. arXiv preprint arXiv:2408.14028, 2024

    arXiv 2024

  6. [6]

    Interactively deformable models for surgery simulation

    Steven A Cover, Norberto F Ezquerra, James F O’Brien, Richard Rowe, Thomas Gadacz, and Ellen Palm. Interactively deformable models for surgery simulation. IEEE Computer Graphics and Applications, 13(6): 68–75, 1993

    work page 1993

  7. [7]

    A semi-implicit material point method for the continuum simulation of granular materials

    Gilles Daviet and Florence Bertails-Descoubes. A semi-implicit material point method for the continuum simulation of granular materials. ACM Transactions on Graphics (TOG), 35(4):1–13, 2016

    work page 2016

  8. [8]

    Modeling the dynamics of a human liver for a minimally inva- sive surgery simulator

    F Boux De Casson and Christian Laugier. Modeling the dynamics of a human liver for a minimally inva- sive surgery simulator. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 1156–1165. Springer, 1999

    work page 1999

Show all 70 references
  1. [9]

    Material point method after 25 years: theory, implementation, and applications

    Alban De Vaucorbeil, Vinh Phu Nguyen, Sina Sinaie, and Jian Ying Wu. Material point method after 25 years: theory, implementation, and applications. Advances in applied mechanics, 53:185–398, 2020

    2020

  2. [10]

    Large scale parallelisation of the material point method with multiple gpus

    Youkou Dong and J¨urgen Grabe. Large scale parallelisation of the material point method with multiple gpus. Computers and Geotechnics, 101:149–158, 2018

    2018

  3. [11]

    K-planes: Explicit radiance fields in space, time, and appearance

    Sara Fridovich-Keil, Giacomo Meanti, Frederik Rahbæk Warburg, Benjamin Recht, and Angjoo Kanazawa. K-planes: Explicit radiance fields in space, time, and appearance. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12479–12488, 2023

    2023

  4. [12]

    A polynomial particle-in-cell method

    Chuyuan Fu, Qi Guo, Theodore Gast, Chenfanfu Jiang, and Joseph Teran. A polynomial particle-in-cell method. ACM Transactions on Graphics (TOG), 36(6):1–12, 2017. 10 Published at Top Journal Arxiv

    2017

  5. [13]

    Simulating arthroscopic knee surgery using volumetric object representations, real-time volume rendering and haptic feedback

    Sarah Gibson, Joe Samosky, Andrew Mor, Christina Fyock, Eric Grimson, Takeo Kanade, Ron Kikinis, Hugh Lauer, Neil McKenzie, Shin Nakajima, et al. Simulating arthroscopic knee surgery using volumetric object representations, real-time volume rendering and haptic feedback. In In...

    1997

  6. [14]

    Han, B.B

    X. Han, B.B. Bai, C.J. Wang, S. Zhao, and Y . Chen. Risk factors for recurrent thrombosis in patients with polycythemia vera and essential thrombocythemia.Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi, 40(1):17 – 23, 2019. Cited by: 1

    2019

  7. [15]

    Antibiotics in surface water and sediments from hanjiang river, central china: Occurrence, behavior and risk assessment

    Ying Hu, Xue Yan, Yun Shen, Mingxiao Di, and Jun Wang. Antibiotics in surface water and sediments from hanjiang river, central china: Occurrence, behavior and risk assessment. Ecotoxicology and Environmental Safety, 157:150–158, 2018. ISSN 0147-6513

    2018

  8. [16]

    A moving least squares material point method with displacement discontinuity and two-way rigid body coupling.ACM Transactions on Graphics (TOG), 37(4):1–14, 2018

    Yuanming Hu, Yu Fang, Ziheng Ge, Ziyin Qu, Yixin Zhu, Andre Pradhana, and Chenfanfu Jiang. A moving least squares material point method with displacement discontinuity and two-way rigid body coupling.ACM Transactions on Graphics (TOG), 37(4):1–14, 2018

    2018

  9. [17]

    Taichi: a lan- guage for high-performance computation on spatially sparse data structures.ACM Transactions on Graphics (TOG), 38(6):201, 2019

    Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, and Fr ´edo Durand. Taichi: a lan- guage for high-performance computation on spatially sparse data structures.ACM Transactions on Graphics (TOG), 38(6):201, 2019

    2019

  10. [18]

    Guided reinforcement learning with efficient exploration for task automation of surgical robot

    Tao Huang, Kai Chen, Bin Li, Yun-Hui Liu, and Qi Dou. Guided reinforcement learning with efficient exploration for task automation of surgical robot. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 4640–4647. IEEE, 2023

    2023

  11. [19]

    Endo- 4dgs: Endoscopic monocular scene reconstruction with 4d gaussian splatting

    Yiming Huang, Beilei Cui, Long Bai, Ziqi Guo, Mengya Xu, Mobarakol Islam, and Hongliang Ren. Endo- 4dgs: Endoscopic monocular scene reconstruction with 4d gaussian splatting. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 197–207...

    2024

  12. [20]

    Anisotropic elastoplasticity for cloth, knit and hair frictional contact

    Chenfanfu Jiang, Theodore Gast, and Joseph Teran. Anisotropic elastoplasticity for cloth, knit and hair frictional contact. ACM Transactions on Graphics, 36(4), 2017. Cited by: 130; All Open Access, Bronze Open Access

    2017

  13. [21]

    A viscohyperelastic maxwell model for rubber viscoelasticity

    AR Johnson and CJ Quigley. A viscohyperelastic maxwell model for rubber viscoelasticity. Rubber chemistry and technology, 65(1):137–153, 1992

    1992

  14. [22]

    Craniofacial surgery simulation

    Erwin Keeve, Sabine Girod, and Bernd Girod. Craniofacial surgery simulation. In International Conference on Visualization in Biomedical Computing, pp. 541–546. Springer, 1996

    1996

  15. [23]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139–1, 2023

    2023

  16. [24]

    Segment anything

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4015–4026, 2023

    2023

  17. [25]

    Dense optical tracking: connecting the dots

    Guillaume Le Moing, Jean Ponce, and Cordelia Schmid. Dense optical tracking: connecting the dots. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19187–19197, 2024

    2024

  18. [26]

    Vr simulation leads to enhanced procedural confidence for surgical trainees

    Heather Lesch, Evan Johnson, J ¨org Peters, and Juan C Cend´an. Vr simulation leads to enhanced procedural confidence for surgical trainees. Journal of surgical education, 77(1):213–218, 2020

    2020

  19. [27]

    Endora: Video generation models as endoscopy simulators.arXiv preprint arXiv:2403.11050, 2024

    Chenxin Li, Hengyu Liu, Yifan Liu, Brandon Y Feng, Wuyang Li, Xinyu Liu, Zhen Chen, Jing Shao, and Yixuan Yuan. Endora: Video generation models as endoscopy simulators.arXiv preprint arXiv:2403.11050, 2024

    2024 arXiv

  20. [28]

    Animation of virtual medical system under the background of virtual reality technology

    Liang Li and Tingting Li. Animation of virtual medical system under the background of virtual reality technology. Computational Intelligence, 38(1):88–105, 2022

    2022

  21. [29]

    Free-dygs: Camera-pose-free scene reconstruction based on gaussian splatting for dynamic surgical videos

    Qian Li, Shuojue Yang, Daiyun Shen, and Yueming Jin. Free-dygs: Camera-pose-free scene reconstruction based on gaussian splatting for dynamic surgical videos. arXiv preprint arXiv:2409.01003, 2024

    2024 arXiv

  22. [30]

    Neural 3d video synthesis from multi-view video

    Tianye Li, Mira Slavcheva, Michael Zollhoefer, Simon Green, Christoph Lassner, Changil Kim, Tan- ner Schmidt, Steven Lovegrove, Michael Goesele, Richard Newcombe, et al. Neural 3d video synthesis from multi-view video. In Proceedings of the IEEE/CVF Conference on Computer Visi...

    2022

  23. [31]

    Spacetime gaussian feature splatting for real-time dynamic view synthesis

    Zhan Li, Zhang Chen, Zhong Li, and Yi Xu. Spacetime gaussian feature splatting for real-time dynamic view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8508–8520, 2024

    2024

  24. [32]

    Towards an end-to-end frame- work for flow-guided video inpainting

    Zhen Li, Cheng-Ze Lu, Jianhua Qin, Chun-Le Guo, and Ming-Ming Cheng. Towards an end-to-end frame- work for flow-guided video inpainting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 17562–17571, 2022

    2022

  25. [33]

    Physics3d: Learning physical properties of 3d gaussians via video diffusion

    Fangfu Liu, Hanyang Wang, Shunyu Yao, Shengjun Zhang, Jie Zhou, and Yueqi Duan. Physics3d: Learning physical properties of 3d gaussians via video diffusion. arXiv preprint arXiv:2406.04338, 2024

    2024 arXiv

  26. [34]

    Endogaussian: Gaussian splatting for deformable surgical scene reconstruction

    Yifan Liu, Chenxin Li, Chen Yang, and Yixuan Yuan. Endogaussian: Gaussian splatting for deformable surgical scene reconstruction. arXiv preprint arXiv:2401.12561, 2024

    2024 arXiv

  27. [35]

    Human-in-the-loop embodied intelli- gence with interactive simulation environment for surgical robot learning

    Yonghao Long, Wang Wei, Tao Huang, Yuehao Wang, and Qi Dou. Human-in-the-loop embodied intelli- gence with interactive simulation environment for surgical robot learning. IEEE Robotics and Automation Letters, 8(8):4441–4448, 2023

    2023

  28. [36]

    3d geometry-aware deformable gaussian splatting for dynamic view synthesis

    Zhicheng Lu, Xiang Guo, Le Hui, Tianrui Chen, Min Yang, Xiao Tang, Feng Zhu, and Yuchao Dai. 3d geometry-aware deformable gaussian splatting for dynamic view synthesis. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8900–8910, 2024

    2024

  29. [37]

    Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis

    Jonathon Luiten, Georgios Kopanas, Bastian Leibe, and Deva Ramanan. Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis. arXiv preprint arXiv:2308.09713, 2023

    2023 arXiv

  30. [38]

    Real-time deformable models for surgery simulation: a survey

    Ullrich Meier, Oscar L ´opez, Carlos Monserrat, M-Carmen Juan, and M Alcaniz. Real-time deformable models for surgery simulation: a survey. Computer methods and programs in biomedicine, 77(3):183–197, 2005

    2005

  31. [39]

    Nerf: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65(1): 99–106, 2021

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65(1): 99–106, 2021

    2021

  32. [40]

    Force sensorless admittance control with neural learning for robots with actuator saturation

    Guangzhu Peng, Chenguang Yang, Wei He, and CL Philip Chen. Force sensorless admittance control with neural learning for robots with actuator saturation. IEEE Transactions on Industrial Electronics, 67(4): 3138–3148, 2019

    2019

  33. [41]

    Dreamfusion: Text-to-3d using 2d diffusion

    Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022

    2022 arXiv

  34. [42]

    Nr-slam: Non-rigid monocular slam

    Juan J G ´omez Rodr´ıguez, Jos´e MM Montiel, and Juan D Tard´os. Nr-slam: Non-rigid monocular slam. IEEE Transactions on Robotics, 2024

    2024

  35. [43]

    Associative coupled thermoplasticity at finite strains: Formulation, numerical analysis and implementation

    Juan C Simo and Christian Miehe. Associative coupled thermoplasticity at finite strains: Formulation, numerical analysis and implementation. Computer Methods in Applied Mechanics and Engineering, 98(1): 41–104, 1992

    1992

  36. [44]

    A material point method for snow simulation

    Alexey Stomakhin, Craig Schroeder, Lawrence Chai, Joseph Teran, and Andrew Selle. A material point method for snow simulation. ACM Transactions on Graphics (TOG), 32(4):1–10, 2013

    2013

  37. [45]

    Bora: Biomedical generalist video generation model

    Weixiang Sun, Xiaocao You, Ruizhe Zheng, Zhengqing Yuan, Xiang Li, Lifang He, Quanzheng Li, and Lichao Sun. Bora: Biomedical generalist video generation model. arXiv preprint arXiv:2407.08944, 2024

    2024 arXiv

  38. [46]

    Endogslam: Real-time dense reconstruction and tracking in endoscopic surgeries using gaussian splatting,

    Kailing Wang, Chen Yang, Yuehao Wang, Sikuang Li, Yan Wang, Qi Dou, Xiaokang Yang, and Wei Shen. Endogslam: Real-time dense reconstruction and tracking in endoscopic surgeries using gaussian splatting,

  39. [47]

    Neural rendering for stereo 3d reconstruction of deformable tissues in robotic surgery

    Yuehao Wang, Yonghao Long, Siu Hin Fan, and Qi Dou. Neural rendering for stereo 3d reconstruction of deformable tissues in robotic surgery. In International conference on medical image computing and computer-assisted intervention, pp. 431–441. Springer, 2022

    2022

  40. [48]

    Cd-mpm: Continuum damage material point methods for dynamic fracture animation

    Joshuah Wolper, Yu Fang, Minchen Li, Jiecong Lu, Ming Gao, and Chenfanfu Jiang. Cd-mpm: Continuum damage material point methods for dynamic fracture animation. ACM Transactions on Graphics, 38(4),

  41. [49]

    4d gaussian splatting for real-time dynamic scene rendering

    Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Xing- gang Wang. 4d gaussian splatting for real-time dynamic scene rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20310–20320, 2024

    2024

  42. [50]

    Physgaussian: Physics-integrated 3d gaussians for generative dynamics

    Tianyi Xie, Zeshun Zong, Yuxing Qiu, Xuan Li, Yutao Feng, Yin Yang, and Chenfanfu Jiang. Physgaussian: Physics-integrated 3d gaussians for generative dynamics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4389–4398, 2024

    2024

  43. [51]

    Sur- gicalgaussian: Deformable 3d gaussians for high-fidelity surgical scene reconstruction

    Weixing Xie, Junfeng Yao, Xianpeng Cao, Qiqin Lin, Zerui Tang, Xiao Dong, and Xiaohu Guo. Sur- gicalgaussian: Deformable 3d gaussians for high-fidelity surgical scene reconstruction. arXiv preprint arXiv:2407.05023, 2024

    2024 arXiv

  44. [52]

    A review of 3d reconstruction techniques for deformable tissues in robotic surgery

    Mengya Xu, Ziqi Guo, An Wang, Long Bai, and Hongliang Ren. A review of 3d reconstruction techniques for deformable tissues in robotic surgery. arXiv preprint arXiv:2408.04426, 2024

    2024 arXiv

  45. [53]

    Efficient deformable tissue reconstruction via orthogonal neural plane

    Chen Yang, Kailing Wang, Yuehao Wang, Qi Dou, Xiaokang Yang, and Wei Shen. Efficient deformable tissue reconstruction via orthogonal neural plane. arXiv preprint arXiv:2312.15253, 2023

    2023 arXiv

  46. [54]

    Neural lerplane representations for fast 4d reconstruction of deformable tissues

    Chen Yang, Kailing Wang, Yuehao Wang, Xiaokang Yang, and Wei Shen. Neural lerplane representations for fast 4d reconstruction of deformable tissues. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 46–56. Springer, 2023

    2023

  47. [55]

    Depth anything v2, 2024

    Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2, 2024. URL https://arxiv.org/abs/2406.09414

    2024 arXiv

  48. [56]

    Deform3dgs: Flexible deformation for fast surgical scene reconstruction with gaussian splatting.arXiv preprint arXiv:2405.17835, 2024

    Shuojue Yang, Qian Li, Daiyun Shen, Bingchen Gong, Qi Dou, and Yueming Jin. Deform3dgs: Flexible deformation for fast surgical scene reconstruction with gaussian splatting.arXiv preprint arXiv:2405.17835, 2024

    2024 arXiv

  49. [57]

    Real-time photorealistic dynamic scene representation and rendering with 4d gaussian splatting

    Zeyu Yang, Hongye Yang, Zijie Pan, Xiatian Zhu, and Li Zhang. Real-time photorealistic dynamic scene representation and rendering with 4d gaussian splatting. arXiv preprint arXiv:2310.10642, 2023

    2023 arXiv

  50. [58]

    Efficient data-driven scene simulation using robotic surgery videos via physics-embedded 3d gaussians

    Zhenya Yang, Kai Chen, Yonghao Long, and Qi Dou. Efficient data-driven scene simulation using robotic surgery videos via physics-embedded 3d gaussians. arXiv preprint arXiv:2405.00956, 2024

    2024 arXiv

  51. [59]

    Endosurf: Neural surface reconstruction of deformable tissues with stereo endoscope videos

    Ruyi Zha, Xuelian Cheng, Hongdong Li, Mehrtash Harandi, and Zongyuan Ge. Endosurf: Neural surface reconstruction of deformable tissues with stereo endoscope videos. In International conference on medical image computing and computer-assisted intervention, pp. 13–23. Springer, 2023

    2023

  52. [60]

    Physdreamer: Physics-based interaction with 3d objects via video generation

    Tianyuan Zhang, Hong-Xing Yu, Rundi Wu, Brandon Y Feng, Changxi Zheng, Noah Snavely, Jiajun Wu, and William T Freeman. Physdreamer: Physics-based interaction with 3d objects via video generation. arXiv preprint arXiv:2404.13026, 2024

    2024 arXiv

  53. [61]

    Deformable endoscopic tissues reconstruction with gaussian splatting

    Lingting Zhu, Zhao Wang, Zhenchao Jin, Guying Lin, and Lequan Yu. Deformable endoscopic tissues reconstruction with gaussian splatting. arXiv preprint arXiv:2401.11535, 2024

    2024 arXiv

  54. [62]

    Neural stress fields for reduced-order elastoplasticity and fracture

    Zeshun Zong, Xuan Li, Minchen Li, Maurizio M Chiaramonte, Wojciech Matusik, Eitan Grinspun, Kevin Carlberg, Chenfanfu Jiang, and Peter Yichen Chen. Neural stress fields for reduced-order elastoplasticity and fracture. In SIGGRAPH Asia 2023 Conference Papers, pp. 1–11, 2023. 13...

    2023

  55. [65]

    Attachment Descriptions (Sec. B)

  56. [66]

    Details of Material Point Method (Sec. C)

  57. [67]

    More Experimental Results (Sec. D)

  58. [68]

    More Implementation Details (Sec. E). B Attachment Descriptions We strongly suggest reviewing our attached HTML page, which contains the following materials:

  59. [69]

    We also provide the complete videos referenced in Fig

    More Visual Results: The video page includes demo videos showing different surgical operations across various real surgical scenes. We also provide the complete videos referenced in Fig. 3 of the paper, alongside quantitative comparisons between four methods (Pcd, Mesh, Baseli...

  60. [70]

    Please click on ‘index.html’ and select ‘Videos’ to view all attachments

    User Study Page: The original interface used in our user study, containing 9 sets of videos for qualitative comparison and Video Guide ablation analysis. Please click on ‘index.html’ and select ‘Videos’ to view all attachments. Figure 6: Visualization of more simulation cases ...

  61. [2019]

    12 Published at Top Journal Arxiv

    Cited by: 70; All Open Access, Bronze Open Access. 12 Published at Top Journal Arxiv

  62. [2024]

    URL https://arxiv.org/abs/2403.15124

    arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.