Neural radiance fields (NeRFs) are a powerful tool for implicit scene representations, allowing for differentiable rendering and the ability to make predictions about previously unseen viewpoints. From a robotics perspective, there has been growing interest in object and scene-based localisation using NeRFs, with a number of recent works relying on sampling-based or Monte-Carlo localisation schemes. Unfortunately, these can be extremely computationally expensive, requiring multiple network forward passes to infer camera or object pose. To alleviate this, a variety of sampling strategies have been applied, many relying on keypoint recognition techniques from classical computer vision. This work conducts a systematic empirical comparison of these approaches and shows that in contrast to conventional feature matching approaches for geometry-based localisation, sampling-based localisation using NeRFs benefits significantly from stable features. Results show that rendering stable features can result in a tenfold reduction in the number of forward passes required, a significant speed improvement.

翻译：神经辐射场（NeRF）是用于隐式场景表示的强大工具，能够实现可微渲染并对未观测视角进行预测。从机器人学视角看，基于NeRF的对象与场景定位方法日益受到关注，近期多项工作依托于采样或蒙特卡洛定位方案。然而这些方法计算开销极大，需多次网络前向传播以推断相机或对象位姿。为缓解此问题，研究者引入多种采样策略，其中多数借鉴经典计算机视觉中的关键点识别技术。本文系统性地对这些方法进行实验对比，发现与基于几何定位的传统特征匹配方法不同，采用稳定特征的NeRF采样定位法能显著提升性能。实验结果表明，渲染稳定特征可使所需前向传播次数降低十倍，实现显著的加速效果。