Research map / 3D vision / NeRF and radiance fields
Improving Physics-Augmented Continuum Neural Radiance Field-Based Geometry-Agnostic System Identification with Lagrangian Particle Optimization
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Proposes Lagrangian particle optimization (LPO), which refines particle positions and features through video sequences within MPM physics, plus iterative geometry-physics optimization, to improve PAC-NeRF geometry-agnostic system identification in sparse-view settings.
Problem
PAC-NeRF's two-step optimization makes geometry and physical identification sensitive to first-frame geometry learning, which fails in sparse-view settings for geometry-agnostic system identification from video.
Approach
Optimize particle positions and features (volume density and color) in Lagrangian space across the whole video under MPM physical constraints, instead of freezing first-frame Eulerian voxel grids, and alternate this with physical re-identification (iterative geometry-physics optimization).
Key results
- In the 3-view setting, +LPO on PAC-NeRF-3v improves PSNR from 27.39 to 29.22 (SSIM 0.978 to 0.980, LPIPS 0.034 to 0.032).
- With iterative geometry-physics optimization (4 iterations), +LPO4 on PAC-NeRF-3v† reaches PSNR 30.34 versus 28.47 for the base PAC-NeRF-3v†.
- LPO improved physical identification in most cases across nine scenes, e.g., Letter log10(µ) error falls from 0.674 (PAC-NeRF-3v) to 0.010 (+LPO4 on PAC-NeRF-3v† variant reported in table).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add LPO to PAC-NeRF-3v | PAC-NeRF-3v · Sparse-view test views | PSNR: 27.39 -> 29.22 | Helped |
| Add LPO to PAC-NeRF-3v† | PAC-NeRF-3v† · Sparse-view test views | PSNR: 28.47 -> 30.11 | Helped |
| Add LPO to PAC-NeRF-3v | PAC-NeRF-3v · Sparse-view test views | LPIPS: 0.034 -> 0.032 | Helped |
| Optimize particle features only instead of both features and positions (ablation) | PAC-NeRF-3v · Sparse-view test views | PSNR: 29.22 -> 28.22 | Hurt |
| Optimize particle positions only instead of both features and positions (ablation) | PAC-NeRF-3v · Sparse-view test views | PSNR: 29.22 -> 28.89 | Hurt |
| Optimize Eulerian voxel grids through the video instead of using Lagrangian particles (ablation) | PAC-NeRF-3v · Sparse-view test views | PSNR: 29.22 -> 28.33 | Hurt |
| Apply four iterative geometry–physics optimization rounds with LPO | PAC-NeRF-3v · Sparse-view test views | PSNR: 27.39 -> 29.65 | Helped |
| Apply four iterative geometry–physics optimization rounds with LPO | PAC-NeRF-3v† · Sparse-view test views | PSNR: 28.47 -> 30.34 | Helped |
| Use LPO for iterative physical re-identification | PAC-NeRF-3v · Letter | log10(µ) absolute error: 0.674 -> 0.015 | Helped |
| Use LPO for iterative physical re-identification | PAC-NeRF-3v · Bird | log10(E) absolute error: 0.449 -> 0.067 | Helped |
| Optimize particle features only during iterative geometry–physics optimization (ablation) | PAC-NeRF-3v · Bird | log10(E) absolute error: 0.067 -> 0.240 | Hurt |
| Optimize particle positions only during iterative geometry–physics optimization (ablation) | PAC-NeRF-3v · Bird | log10(E) absolute error: 0.067 -> 0.074 | Hurt |
Surprising findings
- Repeatedly applying G2P and P2G to update voxel grids produces erosion-like artifacts, so the full recalculation approach was adopted instead.
Limitations
- Training time increases due to LPO and iterative algorithm.
- LPO is sensitive to the state before it is applied (PAC-NeRF-3v or -3v†).
- Evaluated on only one dataset (PAC-NeRF's nine synthetic scenes) with three training views.
Experimental setup
Datasets: PAC-NeRF synthetic dataset (nine scenes)
Benchmarks: PAC-NeRF dataset (nine scenes)
Metrics: PSNR SSIM LPIPS absolute distance of physical properties
Baselines: PAC-NeRF PAC-NeRF-3v PAC-NeRF-3v† +GO +LPO-F +LPO-P +None
Builds on: PAC-NeRF NeRF DiffMPM NeuroFluid
Closest papers by meaning
- Joint Optimization of Neural Radiance Fields and Continuous Camera Motion from a Monocular Video (CVPR 2025 · similarity 0.86)
- PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields (CVPR 2025 · similarity 0.85)
- Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems (CVPR 2025 · similarity 0.85)
- NeuMA: Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics (NeurIPS 2024 · similarity 0.85)
- Structure from Collision (CVPR 2025 · similarity 0.85)
- MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field (ICLR 2024 · similarity 0.84)
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