Node-RF: Learning Generalized Continuous Space-Time Scene Dynamics with Neural ODE-based NeRFs
摘要
Predicting scene dynamics from visual observations is challenging. Existing methods capture dynamics only within observed boundaries failing to extrapolate far beyond the training sequence. Node-RF (Neural ODE-based NeRF) overcomes this limitation by integrating Neural Ordinary Differential Equations (NODEs) with dynamic Neural Radiance Fields (NeRFs), enabling a continuous-time, spatiotemporal representation that generalizes beyond observed trajectories at constant memory cost. From visual input, Node-RF learns an implicit scene state that evolves over time via an ODE solver, propagating feature embeddings via differential calculus. A NeRF-based renderer interprets calculated embeddings to synthesize arbitrary views for long-range extrapolation. Training on multiple motion sequences with shared dynamics allows for generalization to unseen conditions. Our experiments demonstrate that Node-RF can characterize abstract system behavior without explicit model to identify critical points for future predictions.
引用
@article{arxiv.2603.12078,
title = {Node-RF: Learning Generalized Continuous Space-Time Scene Dynamics with Neural ODE-based NeRFs},
author = {Hiran Sarkar and Liming Kuang and Yordanka Velikova and Benjamin Busam},
journal= {arXiv preprint arXiv:2603.12078},
year = {2026}
}
备注
Accepted to CVPR 2026. 13 pages, 9 figures