Node-RF: Learning Generalized Continuous Space-Time Scene Dynamics with Neural ODE-based NeRFs

Hiran Sarkar; Liming Kuang; Yordanka Velikova; Benjamin Busam

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arXiv · 2603.12078

Node-RF: Learning Generalized Continuous Space-Time Scene Dynamics with Neural ODE-based NeRFs

Hiran Sarkar·Liming Kuang·Yordanka Velikova·Benjamin Busam

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Abstract

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.

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Hiran Sarkar, Liming Kuang, Yordanka Velikova, Benjamin Busam. 2026-03-12. Node-RF: Learning Generalized Continuous Space-Time Scene Dynamics with Neural ODE-based NeRFs. https://arxiv.org/abs/2603.12078

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