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Neural radiance fields (NeRFs) are a powerful tool for implicit scenerepresentations, allowing for differentiable rendering and the ability to makepredictions about unseen viewpoints. There has been growing interest in objectand scene-based localisation using NeRFs, with a number of recent works relyingon sampling-based or Monte-Carlo localisation schemes. Unfortunately, these canbe extremely computationally expensive, requiring multiple network forwardpasses to infer camera or object pose. To alleviate this, a variety of samplingstrategies have been applied, many relying on keypoint recognition techniquesfrom classical computer vision. This work conducts a systematic empiricalcomparison of these approaches and shows that in contrast to conventionalfeature matching approaches for geometry-based localisation, sampling-basedlocalisation using NeRFs benefits significantly from stable features. Resultsshow that rendering stable features provides significantly better estimationwith a tenfold reduction in the number of forward passes required.

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