NeRF-Supervised Feature Point Detection and Description

Feature point detection and description is the backbone for various computer vision applications, such as Structure-from-Motion, visual SLAM, and visual place recognition. While learning-based methods have surpassed traditional handcrafted techniques, their training often relies on simplistic homography-based simulations of multi-view perspectives, limiting model generalisability. This paper introduces a novel approach leveraging neural radiance fields (NeRFs) for realistic multi-view training data generation. We create a diverse multi-view dataset using NeRFs, consisting of indoor and outdoor scenes. Our proposed methodology adapts state-of-the-art feature detectors and descriptors to train on NeRF-synthesised views supervised by perspective projective geometry. Our experiments demonstrate that the proposed methods achieve competitive or superior performance on standard benchmarks for relative pose estimation, point cloud registration, and homography estimation while requiring significantly less training data compared to existing approaches.