While existing Neural Radiance Fields (NeRFs) for dynamic scenes are offline methods with an emphasis on visual fidelity, our paper addresses the online use case that prioritises real-time adaptability. We present ParticleNeRF, a new approach that dynamically adapts to changes in the scene geometry by learning an up-to-date representation online, every 200ms. ParticleNeRF achieves this using a novel particle-based parametric encoding. We couple features to particles in space and backpropagate the photometric reconstruction loss into the particles' position gradients, which are then interpreted as velocity vectors. Governed by a lightweight physics system to handle collisions, this lets the features move freely with the changing scene geometry. We demonstrate ParticleNeRF on various dynamic scenes containing translating, rotating, articulated, and deformable objects. ParticleNeRF is the first online dynamic NeRF and achieves fast adaptability with better visual fidelity than brute-force online InstantNGP and other baseline approaches on dynamic scenes with online constraints. Videos of our system can be found at our project website https://sites.google.com/view/particlenerf.

翻译：尽管现有面向动态场景的神经辐射场（NeRF）多为强调视觉保真度的离线方法，但本文聚焦于优先考虑实时适应性的在线应用场景。我们提出ParticleNeRF，一种通过每200ms在线学习最新表示来动态适应场景几何变化的新方法。ParticleNeRF采用一种新颖的粒子基参数化编码实现该目标。我们将特征与空间中的粒子耦合，通过反向传播光度重建损失更新粒子位置梯度，并将其解释为速度向量。通过轻量级物理系统的碰撞处理机制，该特征能随场景几何变化自由移动。我们在包含平移、旋转、铰接及可变形物体的多种动态场景中验证了ParticleNeRF。作为首个在线动态NeRF，ParticleNeRF在满足在线约束的动态场景中，相比暴力式在线InstantNGP及其他基线方法，实现了更快的适应速度与更优的视觉保真度。系统演示视频详见项目网站https://sites.google.com/view/particlenerf。