PhysicsNeRF is a physically grounded framework for 3D reconstruction from sparse views, extending Neural Radiance Fields with four complementary constraints: depth ranking, RegNeRF-style consistency, sparsity priors, and cross-view alignment. While standard NeRFs fail under sparse supervision, PhysicsNeRF employs a compact 0.67M-parameter architecture and achieves 21.4 dB average PSNR using only 8 views, outperforming prior methods. A generalization gap of 5.7-6.2 dB is consistently observed and analyzed, revealing fundamental limitations of sparse-view reconstruction. PhysicsNeRF enables physically consistent, generalizable 3D representations for agent interaction and simulation, and clarifies the expressiveness-generalization trade-off in constrained NeRF models.
View on arXiv@article{barhdadi2025_2505.23481,
title={ PhysicsNeRF: Physics-Guided 3D Reconstruction from Sparse Views },
author={ Mohamed Rayan Barhdadi and Hasan Kurban and Hussein Alnuweiri },
journal={arXiv preprint arXiv:2505.23481},
year={ 2025 }
}