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arXiv · 2309.11698

Rendering Stable Features Improves Sampling-Based Localisation with Neural Radiance Fields

Boxuan Zhang·Lindsay Kleeman·Michael Burke
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Abstract

Neural radiance fields (NeRFs) are a powerful tool for implicit scene representations, allowing for differentiable rendering and the ability to make predictions about unseen viewpoints. 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 provides significantly better estimation with a tenfold reduction in the number of forward passes required.

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Boxuan Zhang, Lindsay Kleeman, Michael Burke. 2023-09-21. Rendering Stable Features Improves Sampling-Based Localisation with Neural Radiance Fields. https://arxiv.org/abs/2309.11698

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