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PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification

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arxiv 2303.05512 v1 pith:HTPYCGEU submitted 2023-03-09 cs.CV cs.AIcs.GRcs.LGcs.RO

Xuan Li , Yi-Ling Qiao , Peter Yichen Chen , Krishna Murthy Jatavallabhula , Ming Lin , Chenfanfu Jiang , Chuang Gan This is my paper · ORCID
classification cs.CVcs.AIcs.GRcs.LGcs.RO
keywords neuralradiancefieldsobjectphysicalcontinuumgeometrypac-nerf
verification ladder T0 review T1 audit T2 compute T3 formal
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Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. This precludes their applicability in a vast majority of scenes where object geometries are complex or unknown. In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology. To this end, we propose "Physics Augmented Continuum Neural Radiance Fields" (PAC-NeRF), to estimate both the unknown geometry and physical parameters of highly dynamic objects from multi-view videos. We design PAC-NeRF to only ever produce physically plausible states by enforcing the neural radiance field to follow the conservation laws of continuum mechanics. For this, we design a hybrid Eulerian-Lagrangian representation of the neural radiance field, i.e., we use the Eulerian grid representation for NeRF density and color fields, while advecting the neural radiance fields via Lagrangian particles. This hybrid Eulerian-Lagrangian representation seamlessly blends efficient neural rendering with the material point method (MPM) for robust differentiable physics simulation. We validate the effectiveness of our proposed framework on geometry and physical parameter estimation over a vast range of materials, including elastic bodies, plasticine, sand, Newtonian and non-Newtonian fluids, and demonstrate significant performance gain on most tasks.

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Cited by 8 Pith papers

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

  1. Volumetric Inverse Rendering via Neural Radiative Transfer

    cs.GR 2026-07 conditional novelty 6.0 of 10

    A physics-informed neural optimization that enforces the Radiative Transfer Equation as a residual recovers volumetric optical properties under global illumination from multi-view images, without explicit global-illum...

  2. VDAWorld: World Modelling via VLM-Directed Abstraction and Simulation

    cs.CV 2025-12 conditional novelty 6.0 of 10

    A vision-language model writes a simulation program—grounded by segmentation and 3D tools—that predicts physically plausible futures from an image and caption, outperforming video generators on modified benchmarks.

  3. Vid2Sim: Generalizable, Video-based Reconstruction of Appearance, Geometry and Physics for Mesh-free Simulation

    cs.GR 2025-06 conditional novelty 6.0 of 10

    Vid2Sim recovers 3D geometry, appearance, and elastic material parameters from multi-view videos using a feed-forward network plus a fast refinement, enabling mesh-free reduced-order simulation.

  4. InteRecon: Towards Reconstructing Interactivity of Personal Memorable Items in Mixed Reality

    cs.HC 2025-02 conditional novelty 6.0 of 10

    The paper demonstrates a prototype that lets people turn cherished objects into interactive AR versions that preserve their original motions, buttons, and embedded media.

  5. BoxTwin: Learning Elastoplastic Articulated Object Dynamics from Videos

    cs.RO 2026-07 reject novelty 4.0 of 10

    BoxTwin models elastoplastic articulated objects as hinged links with nonlinear elastic, plastic, and damage terms, but it does not demonstrate that these dynamics are learned from video beyond qualitative replay.

  6. CA-World: Multi-Object Counterfactual Alignment for Efficient Interactive-Ready Reconstruction

    cs.CV 2026-05 unverdicted novelty 4.0 of 10

    The paper's stated CA-World counterfactual claim is absent from the body, which instead describes the SAM3D-Phys pipeline for multi-object interactive reconstruction and simulation.

  7. Advances in 4D Representation: Geometry, Motion, and Interaction

    cs.CV 2025-10 conditional novelty 4.0 of 10

    A representation-centric survey of 4D generation and reconstruction, organized by geometry, motion, and interaction, with qualitative trade-off comparisons across seven representation families.

  8. DEM-NeRF: A Neuro-Symbolic Method for Scientific Discovery through Physics-Informed Simulation

    cs.LG 2025-07 reject novelty 4.0 of 10

    DEM-NeRF is a pipeline that builds a 3D model from multi-view images with NeRF and predicts hyperelastic deformation with a deep energy method, but it presents no accuracy validation.

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