安定した特徴量のレンダリングによるNeRFベースのサンプリング局所化の改善
Rendering Stable Features Improves Sampling-Based Localisation with Neural Radiance Fields
NeRFを用いたサンプリングベースの自己位置推定において、古典的な特徴マッチングとは異なり安定した特徴量をレンダリングすることが有効であることを示し、順伝播回数を10分の1に削減した。
著者: Boxuan Zhang, Lindsay Kleeman, Michael Burke
分類: cs.RO
原文アブストラクト
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.
