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

2026-03-12

AI Summary

Existing methods struggle to achieve long-term dynamic extrapolation from visual observations, particularly when generalizing beyond the temporal scope of training sequences. This work proposes a continuous spatiotemporal implicit representation framework that integrates Neural Ordinary Differential Equations (NODEs) with dynamic Neural Radiance Fields (NeRF). By leveraging an ODE solver to evolve implicit scene states over time, the approach enables novel view synthesis and long-horizon dynamics prediction with constant memory overhead. Notably, it requires no explicit physical model and represents the first integration of NODEs with dynamic NeRF. The method demonstrates strong generalization to unseen camera trajectories and scene conditions, while accurately identifying critical state transitions in the system’s temporal evolution.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Diffusion Models for Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• scene dynamics
• visual observations
• temporal extrapolation
• spatiotemporal representation
• generalization

Innovation

• Neural ODE
• Neural Radiance Fields
• continuous-time dynamics
• spatiotemporal generalization
• long-range extrapolation