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REVIEW 4 major objections 6 minor 66 references

LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read LiDAR-RT renders dynamic driving scenes as real-time, physically accurate LiDAR views with Gaussian primitives and hardware ray tracing.

desk verdict Real-time LiDAR re-simulation via Gaussian ray tracing works, but the abstract overclaims — the method is rigid-vehicle-only and the quality edge over LiDAR4D is mixed. read the letter →

arxiv 2412.15199 v1 pith:NMT5T5M3 submitted 2024-12-19 cs.CV cs.LGcs.RO

Chenxu Zhou , Lvchang Fu , Sida Peng , Yunzhi Yan , Zhanhua Zhang , Yong Chen , Jiazhi Xia , Xiaowei Zhou This is my paper · ORCID
classification cs.CVcs.LGcs.RO
keywords LiDARre-simulationnovelviewsynthesis3DGaussiansplattingraytracingdynamicscenesautonomousdrivingscenegraphsrangeimages
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

LiDAR-RT is a method for synthesizing new LiDAR views of dynamic driving scenes in real time. The paper's central claim is that a scene represented by Gaussian primitives, with extra learnable parameters for LiDAR reflectance and ray-drop, can be rendered by a hardware-accelerated ray tracer to produce physically realistic range, intensity, and ray-drop images at 20–43 FPS, roughly two orders of magnitude faster than existing neural-field LiDAR simulators and with equal or better quality on the tested benchmarks. The authors decompose scenes into a static background and rigidly moving vehicles whose poses come from tracked 3D bounding boxes, which makes scene editing and sensor reconfiguration straightforward. If the claim holds, real-time closed-loop LiDAR simulation for autonomous driving testing becomes practical with quality close to offline neural rendering.

What carries the argument

The load-bearing object is the Gaussian primitive extended into a LiDAR surface element: a planar 2D Gaussian disk with a proxy geometry of two co-planar triangles, inserted into a BVH built on NVIDIA OptiX, so each sensor ray is intersected against the scene and the sorted hits are volume-rendered in chunks. Motion is handled by scene graphs: every foreground vehicle's Gaussians live in an object-local frame and are mapped to the world by the tracked box rotation and translation, so a single ray tracer renders background and objects together. Differentiability is recovered by re-casting the same rays in the backward pass and using the front-to-back gradient identity from sorted Gaussian splatting.

What would settle it

Take a held-out sequence with a walking pedestrian or cyclist, render the LiDAR view with a trained model, and compare depth and point-cloud Chamfer distance only inside that object's box: if the error is substantially worse than for rigid vehicles, or if adding realistic jitter to the input bounding boxes degrades the rendered range image noticeably, the rigid-box assumption is the failing link.

Watch

Extended reading notes

Core claim

At the center of the paper is the observation that LiDAR's image formation—laser rays, range, intensity, ray-drop—can be reproduced by ray tracing 2D Gaussian disks rather than by rasterizing or querying a neural field. Each Gaussian carries geometric attributes plus spherical-harmonic coefficients that encode view-dependent reflection intensity and a two-logit ray-drop probability; dynamic vehicles are Gaussian sets transformed by rigid poses from tracked boxes; a bounding volume hierarchy of co-planar triangle proxies lets the tracer find sorted intersections per ray, and volumetric alpha blending produces the LiDAR image. A front-to-back gradient rule makes the whole pipeline differentiable, and a U-Net refines sensor-level ray-drop. On Waymo and KITTI-360 the method reports lower depth and intensity error than LiDAR-NeRF, DyNFL, and LiDAR4D on most metrics while rendering a 64×2650 range image at about 20 FPS on Waymo and 42.7 FPS on KITTI-360.

Load-bearing premise

The whole dynamic-object pipeline assumes each moving actor is a rigid body whose 3D bounding-box pose is known and correct at every timestamp, and the paper states that non-rigid objects such as pedestrians and cyclists therefore cannot be modeled accurately.

Editorial extensions

If this is right

  • LiDAR re-simulation for autonomous driving can move from offline neural rendering to real-time closed-loop use, since a 64×2650 range image renders at roughly 20–43 FPS on a single GPU.
  • Scene editing that matters for simulators—moving a vehicle along a new trajectory, inserting an object from another log, or removing one—is a direct operation on the scene graph rather than a retraining step.
  • Sensor configuration changes (beam count, vertical FOV, pose) reduce to changing the ray batch and projection equations, so one learned scene serves many virtual LiDAR designs.
  • The same learned Gaussian scene can be rendered for cameras and LiDAR, opening a path to joint sensor simulation from a single representation.
  • Because training takes about two hours and storage is roughly 0.4–1.4 GB per sequence, the method is practical to apply per recorded log.

Reading between the lines

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

  • A natural extension is to replace the rigid-box pose with a per-Gaussian deformation field so non-rigid actors such as pedestrians and cyclists could be handled; the paper's own stated limitation marks this as the next needed step.
  • The ray-drop model (two logits encoded in spherical harmonics plus a spatial U-Net refinement) is sensor-specific but separable, so the same pipeline could be adapted to other active sensors—radar, sonar, or solid-state LiDAR—by changing the ray generation and drop model.
  • Because quality is measured on range images from two datasets with 64-beam sensors, a strong test of generality would be re-simulation of a 128-beam or solid-state LiDAR pattern, where the SH-based view dependence and the BVH efficiency are both stressed.
  • The method's speed depends on scene size; the paper notes long sequences accumulate Gaussians and slow rendering, so a streaming or compacting strategy would be needed before city-scale logs can be simulated in real time.
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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

4 major / 6 minor

Summary. The paper presents LiDAR-RT, a Gaussian-based ray tracing framework for re-simulating LiDAR scans in dynamic driving scenes. The scene is decomposed into a static background and rigid foreground objects, each represented by 2D Gaussian primitives enriched with learnable intensity and ray-drop parameters. A BVH is constructed over proxy triangle geometry, and an OptiX-based ray tracer casts rays to produce range, intensity, and ray-drop images. The representation is optimized with depth, intensity, ray-drop, and Chamfer distance losses, followed by a UNet-based sensor-level ray-drop refinement. Evaluations on Waymo Open and KITTI-360 report real-time inference (20–43 FPS) and state-of-the-art or competitive quality relative to LiDAR-NeRF, DyNFL, LiDAR4D, PCGen, and LiDARsim, along with applications such as scene editing and sensor re-simulation.

Significance. The claimed contribution—real-time, high-quality LiDAR re-simulation without NeRF-style per-ray network evaluations—is valuable for autonomous driving simulation and sensor modeling. The use of hardware-accelerated ray tracing over Gaussian proxies is a technically sound and potentially influential design, and the paper provides extensive quantitative and qualitative comparisons, ablations, and application demonstrations. The main strengths are the efficient rendering pipeline, the explicit decomposition into background and object models, and the thorough evaluation on two public benchmarks. If the reported numbers hold under independent scrutiny, this is a strong step forward for LiDAR simulation.

major comments (4)
  1. [Section 6, Section 3.2 (Eq. 5)] The dynamic-scene handling is restricted to rigid bodies whose poses are supplied by external tracked bounding boxes. The paper's own limitation statement concedes that "LiDAR-RT cannot accurately model non-rigid objects such as pedestrians and cyclists." Since these actors appear routinely in driving scenes, the abstract's unqualified claim that the method supports "physically accurate LiDAR re-simulation for driving scenes" overstates the validated scope. I recommend rewording the claims to focus on rigid vehicles or specifying the required tracking inputs, and to add an explicit statement in the abstract about this scope. Moreover, no experiment measures sensitivity to tracking noise or box jitter, even though Eq. 5 makes these inputs load-bearing; a perturbation study would substantiate the practical robustness claims.
  2. [Section 5.1, Table 2] On KITTI-360, the ground-truth range images are not raw scans but are obtained by fusing multi-frame point clouds and projecting them. This protocol may systematically favor representations that reconstruct an averaged geometry (e.g., Gaussian splatting) over single-scan-based methods, and it makes absolute metric values incomparable with those reported on raw scans elsewhere. Please justify this protocol or validate the ranking on at least a subset with raw per-frame scans, and explicitly discuss the potential bias.
  3. [Section 5.1, Table 2] LiDARsim and PCGen are re-implemented by the authors because official code is unavailable. The re-implementations yield notably poor performance (e.g., LiDARsim CD of 3.22 on KITTI-360), which raises the question of whether the baseline configurations are reasonably tuned. Please provide the exact re-implementation settings, release the baseline code or configurations, and, if possible, cross-validate the implementations with the original authors or with numbers reported in the original papers.
  4. [Section 3.3, Section 4] The real-time performance claim depends on several hyperparameters—chunk size 16, transmittance threshold T_min, near plane 0.2—but the paper provides no ablation of these values on the FPS/quality trade-off. Additionally, it is unclear whether the reported FPS includes the UNet refinement stage and whether the refinement runs at inference time on every frame. Please report the complete inference pipeline timing and a sensitivity analysis of the traversal parameters.
minor comments (6)
  1. [Section 1] The phrase "to tickle the challenges" is a typo; it should be "to tackle the challenges."
  2. [Section 4, Figure 1] Section 4 states training for 30,000 iterations on one RTX 4090, while Figure 1 says "within 2 hours of training"; please clarify the actual training time and hardware specifics.
  3. [Tables 3, 6, 7] The ablation tables report quality metrics but not FPS or storage; adding these columns would strengthen the efficiency claims and allow direct comparison with Table 1 and Table 2.
  4. [Section 5.1] Please clarify whether the baseline FPS values are measured on the same GPU as the proposed method, and describe the exact measurement conditions for all efficiency numbers.
  5. [Supplementary, Table 2] In Table 2, PCGen's intensity PSNR of 14.12 with SSIM 0.1351 appears an outlier; please double-check these values and provide visual examples if possible.
  6. [General] The paper would benefit from a statement about code and model release; the implementation details are otherwise extensive and would support reproducibility if the code were made available.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the quality claims are evaluated on held-out frames against real LiDAR scans, and the only author-overlapping citation is a non-load-bearing implementation detail.

full rationale

The paper is a fitting-and-rendering system rather than a formal derivation, and its central comparisons are not circular by construction. In Sec. 5.1 the authors state: 'we sample every 10th frame in the sequence as the test frames and use the remaining for training' (Waymo; the same strategy is used for KITTI-360). The metrics in Tables 1 and 2 are therefore computed on scans that were not used to optimize the Gaussian parameters, the LiDAR property SH coefficients, or the ray-drop refinement UNet; novel-view renderings are produced by ray-casting through a scene representation learned from other frames. No equation defines a test-frame quantity as the output of the fitting procedure: the losses in Eq. 10 supervise the reconstruction from training scans, and the held-out test frames provide extrinsic evaluation against real LiDAR data. The only author-overlapping citation, [50] (Street Gaussians), is used for the standard rigid-object transform 'same as [43, 50]' in Eq. 5 and for an object-point sampling strategy in Suppl. A.3; neither is load-bearing for the reported quality or efficiency results. The rigid-body transform is a conventional scene-graph choice, and the sampling trick does not define any reported metric. The admitted limitation in Sec. 6 ('LiDAR-RT cannot accurately model non-rigid objects such as pedestrians and cyclists') narrows the scope of the abstract's 'driving scenes' claim, but this is a correctness/scope caveat, not a circular dependency. I find no self-definitional step, no fitted input renamed as a prediction, and no imported uniqueness theorem or ansatz smuggled in via citation.

Assumptions & free parameters 7 free parameters · 6 assumptions · 0 invented entities

The method is a fitting pipeline, so most learnable parameters are the scene representation being optimized. The free parameters that matter for the central claim are the hand-set hyperparameters listed above. The modeling assumptions (rigid tracked objects, SH-based intensity and ray-drop, separable ray-drop categories, triangular proxy geometry) are introduced without independent physical validation beyond benchmark metrics. No new physical entities are postulated.

free parameters (7)
  • Loss weights lambda_d, lambda_i, lambda_r, lambda_CD = 0.1, 0.1, 0.01, 0.01
    Set by hand (Section 4); balance depth, intensity, ray-drop, and Chamfer losses during optimization.
  • Voxel downsampling size = 0.15 m
    Used to downsample fused multi-frame LiDAR point clouds for background initialization (Section 3.2).
  • Ray tracing chunk size = 16
    Number of intersections per sorting chunk during ray marching (Sections 3.3 and 4); trades memory against efficiency.
  • Near plane = 0.2
    Ray tracing near plane in meters (Section 4).
  • Object model point target = 8K points
    Object models with fewer points are padded with random samples inside the 3D bounding box to reach 8K points (Section 3.2).
  • Transmittance threshold T_min = not reported
    Predefined threshold for early termination of ray marching in the forward pass (Section 3.3); value not given in the paper.
  • UNet refinement epochs and learning rate = 500 epochs, 1e-3
    Hyperparameters for the ray-drop refinement network (Section 4).
assumptions (6)
  • domain assumption Alpha-compositing volumetric rendering of Gaussian primitives (Eq. 2 to 3) is a valid sensor model for LiDAR
    The paper replaces color ci with LiDAR intensity zi and ray-drop probability bi and accumulates along rays; this assumes a linear blending model is adequate for active laser sensing.
  • domain assumption LiDAR intensity and ray-drop are functions of view direction and can be represented with spherical harmonics
    Section 3.2 models (zeta, beta_drop, beta_hit) with SHs; no physical BRDF or beam geometry is modeled.
  • domain assumption Dynamic objects are rigid and their trajectories are known from tracked bounding boxes
    Eq. 5 applies only rigid rotations and translations; the paper's Section 6 states the method fails on non-rigid objects.
  • ad hoc to paper Ray-drop separates into scene-level (learned per-Gaussian) and sensor-level (UNet-refined) components
    Section 3.5 introduces this categorization 'through our practice'; it is a modeling choice, not derived.
  • ad hoc to paper A 2D planar Gaussian disk wrapped by two co-planar triangles is an adequate proxy geometry for LiDAR ray intersections
    Section 3.3 chooses this proxy for BVH efficiency; the ablation in Sec. B.1 shows 2D Gaussians outperform 3D Gaussian icosahedra on their metrics but does not validate physical accuracy.
  • standard math Cylindrical range image projection (Eq. 7) with uniform azimuth and elevation mapping correctly represents the sensor
    Standard LiDAR range-image projection used by previous works; also adopted in the evaluation.

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Cite this review

Pith. "Pith review of LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation." pith.science (2026). https://pith.science/paper/NMT5T5M3

@misc{pith2026241215199,
  author       = {Pith},
  title        = {Pith review of: LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NMT5T5M3}},
  note         = {Machine review of arXiv:2412.15199}
}
read the original abstract

This paper targets the challenge of real-time LiDAR re-simulation in dynamic driving scenarios. Recent approaches utilize neural radiance fields combined with the physical modeling of LiDAR sensors to achieve high-fidelity re-simulation results. Unfortunately, these methods face limitations due to high computational demands in large-scale scenes and cannot perform real-time LiDAR rendering. To overcome these constraints, we propose LiDAR-RT, a novel framework that supports real-time, physically accurate LiDAR re-simulation for driving scenes. Our primary contribution is the development of an efficient and effective rendering pipeline, which integrates Gaussian primitives and hardware-accelerated ray tracing technology. Specifically, we model the physical properties of LiDAR sensors using Gaussian primitives with learnable parameters and incorporate scene graphs to handle scene dynamics. Building upon this scene representation, our framework first constructs a bounding volume hierarchy (BVH), then casts rays for each pixel and generates novel LiDAR views through a differentiable rendering algorithm. Importantly, our framework supports realistic rendering with flexible scene editing operations and various sensor configurations. Extensive experiments across multiple public benchmarks demonstrate that our method outperforms state-of-the-art methods in terms of rendering quality and efficiency. Our project page is at https://zju3dv.github.io/lidar-rt.

Figures

Figures reproduced from arXiv: 2412.15199 by the authors.

Figure 1
Figure 1. Realistic and real-time rendering of LiDAR view in dynamic driving scenes. Our LiDAR-RT produces high-fidelity LiDAR view at 30 FPS (64×2650) within 2 hours of training. SOTA method [58] struggles to model the dynamic objects in complex scenes and suffers from high training and rendering costs (15 hours for training and 0.2 FPS for rendering a range image). Abstract This paper targets the challenge of real-time LiDA… view at source ↗
Figure 2
Figure 2. Overview of LiDAR-RT. (a) We decompose the dynamic scene into a background model and multiple object models, with each represented by a set of Gaussian primitives. In addition to geometric attributes, we introduce learnable parameters (SHs) on Gaussians to emulate the intrinsic properties (ζ, β) of LiDAR sensors. (b) Based on this representation, we design a differentiable ray tracing framework. We first construct t… view at source ↗
Figure 3
Figure 3. Ray intersection with proxy geometries. We construct the proxy geometry for each 2D Gaussian primitive as a pair of co-planar triangles, then the ray tracer performs intersection tests with the vertices ⃗v and surface normal ⃗n. Proxy Geometries. In the realm of traditional graphics rendering [1, 22], proxy geometries serve as the simpli￾fied representations to approximate the complex original geometries, which can … view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison on the KITTI-360 [20] dataset. Dynamic vehicles are zoomed in for better visualization. 3DGS [16] meets this need by using per-tile sorted buffers. However, this approach is not available for us due to the distinction between the panoramic LiDAR …
Figure 5
Figure 5. Figure 5: Scene editing results on the Waymo dataset [40]. Our method supports various scene editing operations, including trajectory manipulation, object insertion, and object removal. The boxes (□/□) highlight the edited objects. 5.2. Comparisons with the State-of-the-art Tabs…
Figure 6
Figure 6. Figure 6: LiDAR re-simulation results with different sensor configurations on the Waymo dataset [40]. Our LiDAR-RT can generate realistic LiDAR point clouds with varying sensor poses, beam numbers, and FOV settings. 6. Conclusion and Discussion In this paper, we propose LiDAR-RT…
Figure 7
Figure 7. Figure 7: Qualitative comparison of novel view LiDAR point clouds on Waymo Open Dataset [40]. Our LiDAR-RT generates a realistic novel LiDAR view with accurate scene geometry and high-frequency details of dynamic objects. In this supplementary material, we begin by presenting ad…
Figure 8
Figure 8. Figure 8: Qualitative results of ablation study on ray tracing with Gaussian variants. Ground Truth w/o rayhit w/o refine w/o spatial refine Ours [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: Qualitative results of ablation study on ray-drop modeling and refinement. Gaussians associated with sampled points that fall outside the bounding box are pruned to avoid excessive growth [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: Qualitative comparison of LiDAR range images on Waymo Open Dataset [40] sequence seg-132712. DyNFL Ground Truth LiDAR-NeRF LiDAR4D LiDAR-RT (ours) DyNFL Ground Truth LiDAR-NeRF LiDAR4D LiDAR-RT (ours) [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: Qualitative comparison of LiDAR range images on Waymo Open Dataset [40] sequence seg-108305. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_11.png]
Figure 12
Figure 12. Figure 12: Decomposition results on Waymo dataset [40]. The points are colorized by intensity values from blue(0) to red (1). 15 [PITH_FULL_IMAGE:figures/full_fig_p015_12.png]

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Reference graph

Works this paper leans on

66 extracted references · 53 canonical work pages

  1. [1]

    Neural point-based graph- ics, 2020

    Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graph- ics, 2020. 5

    work page 2020

  2. [2]

    HyperReel: High-fidelity 6-DoF video with ray- conditioned sampling

    Benjamin Attal, Jia-Bin Huang, Christian Richardt, Michael Zollhoefer, Johannes Kopf, Matthew O’Toole, and Changil Kim. HyperReel: High-fidelity 6-DoF video with ray- conditioned sampling. In CVPR, 2023. 2

    work page 2023

  3. [3]

    Callahan, Aaron Lefohn, Jo ˜ao L

    Louis Bavoil, Steven P. Callahan, Aaron Lefohn, Jo ˜ao L. D. Comba, and Cl´audio T. Silva. Multi-fragment effects on the gpu using the k-buffer. In Proceedings of the 2007 Sympo- sium on Interactive 3D Graphics and Games , page 97–104, New York, NY , USA, 2007. Association for Computing Ma- chinery. 2

    work page 2007

  4. [4]

    The lov ´asz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks

    Maxim Berman, Amal Rannen Triki, and Matthew B Blaschko. The lov ´asz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In CVPR, pages 4413–4421, 2018. 4

    work page 2018

  5. [5]

    RayGauss: V olumetric gaussian-based ray casting for photo- realistic novel view synthesis, 2024

    Hugo Blanc, Jean-Emmanuel Deschaud, and Alexis Paljic. RayGauss: V olumetric gaussian-based ray casting for photo- realistic novel view synthesis, 2024. 3, 5

    work page 2024

  6. [6]

    Deep generative modeling of LiDAR data, 2019

    Lucas Caccia, Herke van Hoof, Aaron Courville, and Joelle Pineau. Deep generative modeling of LiDAR data, 2019. 2

    work page 2019

  7. [7]

    Periodic vibration gaussian: Dynamic urban scene reconstruction and real-time rendering

    Yurui Chen, Chun Gu, Junzhe Jiang, Xiatian Zhu, and Li Zhang. Periodic vibration gaussian: Dynamic urban scene reconstruction and real-time rendering. arXiv:2311.18561,

    arXiv

  8. [8]

    OmniRe: Omni urban scene reconstruction

    Ziyu Chen, Jiawei Yang, Jiahui Huang, Riccardo de Lutio, Janick Martinez Esturo, Boris Ivanovic, Or Litany, Zan Go- jcic, Sanja Fidler, Marco Pavone, Li Song, and Yue Wang. OmniRe: Omni urban scene reconstruction. arXiv preprint arXiv:2408.16760, 2024. 1, 3

    arXiv 2024

Show all 66 references
  1. [9]

    CARLA: An open urban driving simulator

    Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In Proceedings of the 1st Annual Conference on Robot Learning, pages 1–16, 2017. 2

    2017

  2. [10]

    A point set generation network for 3D object reconstruction from a sin- gle image, 2016

    Haoqiang Fan, Hao Su, and Leonidas Guibas. A point set generation network for 3D object reconstruction from a sin- gle image, 2016. 6, 7

    2016

  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 CVPR, 2023. 2

    2023

  4. [12]

    Relightable 3d gaussian: Real-time point cloud relighting with brdf decomposition and ray trac- ing

    Jian Gao, Chun Gu, Youtian Lin, Hao Zhu, Xun Cao, Li Zhang, and Yao Yao. Relightable 3d gaussian: Real-time point cloud relighting with brdf decomposition and ray trac- ing. arXiv:2311.16043, 2023. 3, 5

    2023 arXiv

  5. [13]

    GPGPU processing in CUDA architec- ture

    Jayshree Ghorpade. GPGPU processing in CUDA architec- ture. Advanced Computing: An International Journal , 3(1): 105–120, 2012. 7

    2012

  6. [14]

    2D gaussian splatting for geometrically ac- curate radiance fields

    Binbin Huang, Zehao Yu, Anpei Chen, Andreas Geiger, and Shenghua Gao. 2D gaussian splatting for geometrically ac- curate radiance fields. In SIGGRAPH 2024 Conference Pa- pers. Association for Computing Machinery, 2024. 2, 3, 5, 13

    2024

  7. [15]

    Neural LiDAR fields for novel view synthesis

    Shengyu Huang, Zan Gojcic, Zian Wang, Francis Williams, Yoni Kasten, Sanja Fidler, Konrad Schindler, and Or Litany. Neural LiDAR fields for novel view synthesis. In ICCV,

  8. [16]

    3D gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimkuehler, and George Drettakis. 3D gaussian splatting for real-time radiance field rendering. TOG, 42(4):139:1–139:14, 2023. 2, 3, 4, 5, 6, 7, 12, 13

    2023

  9. [17]

    Adam: A method for stochastic optimization

    Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 ,

    arXiv

  10. [18]

    Efficient particle volume splatting in a ray tracer

    Aaron Knoll, R Keith Morley, Ingo Wald, Nick Leaf, and Peter Messmer. Efficient particle volume splatting in a ray tracer. Ray Tracing Gems: High-Quality and Real-Time Ren- dering with DXR and Other APIs, pages 533–541, 2019. 5

    2019

  11. [19]

    PCGen: Point cloud generator for lidar simulation

    Chenqi Li, Yuan Ren, and Bingbing Liu. PCGen: Point cloud generator for lidar simulation. InICRA, pages 11676–11682,

  12. [20]

    Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d

    Yiyi Liao, Jun Xie, and Andreas Geiger. Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3):3292–3310, 2022. 2, 6, 7, 8, 12

    2022

  13. [21]

    Are nerfs ready for autonomous driving? towards closing the real-to-simulation gap

    Carl Lindstr ¨om, Georg Hess, Adam Lilja, Maryam Fatemi, Lars Hammarstrand, Christoffer Petersson, and Lennart Svensson. Are nerfs ready for autonomous driving? towards closing the real-to-simulation gap. In CVPRW, 2024. 2

    2024

  14. [22]

    Mix- ture of volumetric primitives for efficient neural rendering

    Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Michael Zollhoefer, Yaser Sheikh, and Jason Saragih. Mix- ture of volumetric primitives for efficient neural rendering. ACM Trans. Graph., 40(4), 2021. 5

    2021

  15. [23]

    Dynamic 3d gaussians: Tracking by per- sistent dynamic view synthesis

    Jonathon Luiten, Georgios Kopanas, Bastian Leibe, and Deva Ramanan. Dynamic 3d gaussians: Tracking by per- sistent dynamic view synthesis. In 3DV, 2024. 3

    2024

  16. [24]

    EVER: Exact volumetric ellip- soid rendering for real-time view synthesis, 2024

    Alexander Mai, Peter Hedman, George Kopanas, Dor Verbin, David Futschik, Qiangeng Xu, Falko Kuester, Jon Barron, and Yinda Zhang. EVER: Exact volumetric ellip- soid rendering for real-time view synthesis, 2024. 3, 5

    2024

  17. [25]

    LiDARsim: Realistic li- dar simulation by leveraging the real world

    Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong, Wenyuan Zeng, Mikita Sazanovich, Shuhan Tan, Bin Yang, Wei-Chiu Ma, and Raquel Urtasun. LiDARsim: Realistic li- dar simulation by leveraging the real world. In CVPR, pages 11167–11176, 2020. 2, 7, 12

    2020

  18. [26]

    Towards zero domain gap: A comprehensive study of realistic lidar simulation for autonomy testing

    Sivabalan Manivasagam, Ioan Andrei B ˆarsan, Jingkang Wang, Ze Yang, and Raquel Urtasun. Towards zero domain gap: A comprehensive study of realistic lidar simulation for autonomy testing. In ICCV, pages 8238–8248, 2023. 2, 4 9

    2023

  19. [27]

    Srinivasan, Matthew Tancik, Jonathan T

    Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view syn- thesis. In ECCV, page 405–421, 2020. 2

    2020

  20. [28]

    3D Gaussian Ray Tracing: Fast tracing of particle scenes

    Nicolas Moenne-Loccoz, Ashkan Mirzaei, Or Perel, Ric- cardo de Lutio, Janick Martinez Esturo, Gavriel State, Sanja Fidler, Nicholas Sharp, and Zan Gojcic. 3D Gaussian Ray Tracing: Fast tracing of particle scenes. ACM Transactions on Graphics and SIGGRAPH Asia, 2024. 3, 5, 13

    2024

  21. [29]

    Neural scene graphs for dynamic scenes

    Julian Ost, Fahim Mannan, Nils Thuerey, Julian Knodt, and Felix Heide. Neural scene graphs for dynamic scenes. In CVPR, pages 2856–2865, 2021. 2, 3

    2021

  22. [30]

    Barron, Sofien Bouaziz, Dan B Goldman, Steven M

    Keunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Steven M. Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. ICCV, 2021. 2

    2021

  23. [31]

    Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin- Brualla, and Steven M

    Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin- Brualla, and Steven M. Seitz. HyperNeRF: A higher- dimensional representation for topologically varying neural radiance fields. ACM Trans. Graph., 40(6), 2021. 2

    2021

  24. [32]

    Parker, James Bigler, Andreas Dietrich, Heiko Friedrich, Jared Hoberock, David Luebke, David McAllis- ter, Morgan McGuire, Keith Morley, Austin Robison, and Martin Stich

    Steven G. Parker, James Bigler, Andreas Dietrich, Heiko Friedrich, Jared Hoberock, David Luebke, David McAllis- ter, Morgan McGuire, Keith Morley, Austin Robison, and Martin Stich. Optix: a general purpose ray tracing engine. ACM Trans. Graph., 29(4), 2010. 5, 7

    2010

  25. [33]

    PyTorch: An im- perative style, high-performance deep learning library, 2019

    Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas K ¨opf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu F...

    2019

  26. [34]

    Surfels: surface elements as rendering primi- tives

    Hanspeter Pfister, Matthias Zwicker, Jeroen van Baar, and Markus Gross. Surfels: surface elements as rendering primi- tives. In Proceedings of the 27th Annual Conference on Com- puter Graphics and Interactive Techniques , page 335–342, USA, 2000. ACM Press/Addison-Wesley Publi...

    2000

  27. [35]

    D-NeRF: Neural Radiance Fields for Dynamic Scenes

    Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-NeRF: Neural Radiance Fields for Dynamic Scenes. In CVPR, 2020. 2

    2020

  28. [36]

    StopThePop: Sorted gaussian splatting for view-consistent real-time rendering

    Lukas Radl, Michael Steiner, Mathias Parger, Alexan- der Weinrauch, Bernhard Kerbl, and Markus Steinberger. StopThePop: Sorted gaussian splatting for view-consistent real-time rendering. ACM Trans. Graph., 43(4), 2024. 3, 6

    2024

  29. [37]

    An efficient represen- tation for irradiance environment maps

    Ravi Ramamoorthi and Pat Hanrahan. An efficient represen- tation for irradiance environment maps. In Proceedings of the 28th Annual Conference on Computer Graphics and In- teractive Techniques, page 497–500, New York, NY , USA,

  30. [38]

    U- Net: Convolutional networks for biomedical image segmen- tation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- Net: Convolutional networks for biomedical image segmen- tation. In MICCAI, pages 234–241, Cham, 2015. Springer International Publishing. 6, 7

    2015

  31. [39]

    AirSim: High-fidelity visual and physical simula- tion for autonomous vehicles, 2017

    Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. AirSim: High-fidelity visual and physical simula- tion for autonomous vehicles, 2017. 2

    2017

  32. [40]

    Scalability in perception for autonomous driving: Waymo open dataset

    Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. In CVPR, pages 2446–2454, 2020. 2, 7, 8, 12, 14, 15

    2020

  33. [41]

    AlignMiF: Geometry-aligned multimodal implicit field for lidar-camera joint synthesis

    Tao Tang, Guangrun Wang, Yixing Lao, Peng Chen, Jie Liu, Liang Lin, Kaicheng Yu, and Xiaodan Liang. AlignMiF: Geometry-aligned multimodal implicit field for lidar-camera joint synthesis. arXiv preprint arXiv:2402.17483, 2024. 2

    2024 arXiv

  34. [42]

    LiDAR-NeRF: Novel lidar view synthesis via neural radiance fields.arXiv preprint arXiv:2304.10406, 2023

    Tang Tao, Longfei Gao, Guangrun Wang, Yixing Lao, Peng Chen, Hengshuang Zhao, Dayang Hao, Xiaodan Liang, Mathieu Salzmann, and Kaicheng Yu. LiDAR-NeRF: Novel lidar view synthesis via neural radiance fields.arXiv preprint arXiv:2304.10406, 2023. 2, 4, 7, 8, 12, 15

    2023 arXiv

  35. [43]

    NeuRAD: Neural rendering for autonomous driving

    Adam Tonderski, Carl Lindstr ¨om, Georg Hess, William Ljungbergh, Lennart Svensson, and Christoffer Petersson. NeuRAD: Neural rendering for autonomous driving. In CVPR, 2024. 2, 3, 4

    2024

  36. [44]

    SUDS: Scalable urban dynamic scenes

    Haithem Turki, Jason Y Zhang, Francesco Ferroni, and Deva Ramanan. SUDS: Scalable urban dynamic scenes. In CVPR,

  37. [45]

    Bovik, H.R

    Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing , 13(4): 600–612, 2004. 7

    2004

  38. [46]

    Dynamic lidar re- simulation using compositional neural fields

    Hanfeng Wu, Xingxing Zuo, Stefan Leutenegger, Or Litany, Konrad Schindler, and Shengyu Huang. Dynamic lidar re- simulation using compositional neural fields. In CVPR,

  39. [47]

    MARS: An instance-aware, modular and realistic simulator for au- tonomous driving

    Zirui Wu, Tianyu Liu, Liyi Luo, Zhide Zhong, Jianteng Chen, Hongmin Xiao, Chao Hou, Haozhe Lou, Yuan- tao Chen, Runyi Yang, Yuxin Huang, Xiaoyu Ye, Zike Yan, Yongliang Shi, Yiyi Liao, and Hao Zhao. MARS: An instance-aware, modular and realistic simulator for au- tonomous drivi...

    2023

  40. [48]

    S-NeRF: Neural radiance fields for street views

    Ziyang Xie, Junge Zhang, Wenye Li, Feihu Zhang, and Li Zhang. S-NeRF: Neural radiance fields for street views. In ICLR 2023, 2023. 2

    2023

  41. [49]

    GeoNLF: Geometry guided pose-free neural lidar fields, 2024

    Weiyi Xue, Zehan Zheng, Fan Lu, Haiyun Wei, Guang Chen, and Changjun Jiang. GeoNLF: Geometry guided pose-free neural lidar fields, 2024. 2

    2024

  42. [50]

    Street gaussians for modeling dynamic ur- ban scenes

    Yunzhi Yan, Haotong Lin, Chenxu Zhou, Weijie Wang, Haiyang Sun, Kun Zhan, Xianpeng Lang, Xiaowei Zhou, and Sida Peng. Street gaussians for modeling dynamic ur- ban scenes. In ECCV, 2024. 1, 3, 4, 12

    2024

  43. [51]

    EmerNeRF: Emergent spatial-temporal scene decomposition via self-supervision

    Jiawei Yang, Boris Ivanovic, Or Litany, Xinshuo Weng, Se- ung Wook Kim, Boyi Li, Tong Che, Danfei Xu, Sanja Fi- dler, Marco Pavone, and Yue Wang. EmerNeRF: Emergent spatial-temporal scene decomposition via self-supervision. In ICLR, 2024. 3

    2024

  44. [52]

    Surfelgan: Synthesizing realistic sensor data for autonomous driving

    Zhenpei Yang, Yuning Chai, Dragomir Anguelov, Yin Zhou, Pei Sun, Dumitru Erhan, Sean Rafferty, and Henrik Kret- zschmar. Surfelgan: Synthesizing realistic sensor data for autonomous driving. In CVPR, 2020. 2

    2020

  45. [53]

    UniSim: A neural closed-loop sensor simulator

    Ze Yang, Yun Chen, Jingkang Wang, Sivabalan Mani- vasagam, Wei-Chiu Ma, Anqi Joyce Yang, and Raquel Ur- tasun. UniSim: A neural closed-loop sensor simulator. In CVPR, 2023. 2, 3 10

    2023

  46. [54]

    AbsGS: Recovering fine details for 3d gaussian splat- ting, 2024

    Zongxin Ye, Wenyu Li, Sidun Liu, Peng Qiao, and Yong Dou. AbsGS: Recovering fine details for 3d gaussian splat- ting, 2024. 13

    2024

  47. [55]

    Mip-Splatting: Alias-free 3d gaussian splat- ting

    Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-Splatting: Alias-free 3d gaussian splat- ting. CVPR, 2024. 3, 13

    2024

  48. [56]

    NeRF-LiDAR: Generating realistic lidar point clouds with neural radiance fields, 2024

    Junge Zhang, Feihu Zhang, Shaochen Kuang, and Li Zhang. NeRF-LiDAR: Generating realistic lidar point clouds with neural radiance fields, 2024. 2

    2024

  49. [57]

    Efros, Eli Shecht- man, and Oliver Wang

    Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shecht- man, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 7

    2018

  50. [58]

    LiDAR4D: Dynamic neural fields for novel space-time view lidar synthesis

    Zehan Zheng, Fan Lu, Weiyi Xue, Guang Chen, and Changjun Jiang. LiDAR4D: Dynamic neural fields for novel space-time view lidar synthesis. InCVPR, pages 5145–5154,

  51. [59]

    HUGS: Holistic urban 3d scene understanding via gaussian splatting

    Hongyu Zhou, Jiahao Shao, Lu Xu, Dongfeng Bai, Weichao Qiu, Bingbing Liu, Yue Wang, Andreas Geiger, and Yiyi Liao. HUGS: Holistic urban 3d scene understanding via gaussian splatting. In CVPR, pages 21336–21345, 2024. 1, 3

    2024

  52. [60]

    Drivinggaussian: Composite gaussian splatting for surrounding dynamic au- tonomous driving scenes

    Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, and Ming-Hsuan Yang. Drivinggaussian: Composite gaussian splatting for surrounding dynamic au- tonomous driving scenes. arXiv preprint arXiv:2312.07920,

    arXiv

  53. [61]

    Zwicker, H

    M. Zwicker, H. Pfister, J. van Baar, and M. Gross. Ewa vol- ume splatting. In Proceedings Visualization, 2001. VIS ’01., pages 29–538, 2001. 4

    2001

  54. [62]

    Learning to generate realistic lidar point clouds

    Vlas Zyrianov, Xiyue Zhu, and Shenlong Wang. Learning to generate realistic lidar point clouds. In ECCV, 2022. 2

    2022

  55. [63]

    LidarDM: Generative lidar simulation in a generated world,

    Vlas Zyrianov, Henry Che, Zhijian Liu, and Shenlong Wang. LidarDM: Generative lidar simulation in a generated world,

  56. [66]

    Qualitative comparison of novel view LiDAR point clouds on Waymo Open Dataset [40]

    2 11 LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation Supplementary Material Ground Truth LiDAR-NeRF DyNFL LiDAR4D Ours Figure 7. Qualitative comparison of novel view LiDAR point clouds on Waymo Open Dataset [40]. Our LiDAR-RT generates a realistic novel Li...

    1908

  57. [2001]

    Association for Computing Machinery. 4

  58. [2024]

    1, 2, 4, 6, 7, 8, 12, 15

Pith tools

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