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arXiv · 2505.23481

PhysicsNeRF: Physics-Guided 3D Reconstruction from Sparse Views

Mohamed Rayan Barhdadi·Hasan Kurban·Hussein Alnuweiri
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Abstract

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.

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Mohamed Rayan Barhdadi, Hasan Kurban, Hussein Alnuweiri. 2025-06-21. PhysicsNeRF: Physics-Guided 3D Reconstruction from Sparse Views. https://arxiv.org/abs/2505.23481

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