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.

该论文引入了一种基于神经辐射场（NeRFs）的新方法，用于生成逼真的多视角训练数据。通过 NeRFs 创建了多样化的室内和室外多视角数据集，并利用透视投影几何指导下的最先进的特征检测器和描述器进行训练。实验证明，该方法在相对位姿估计、点云配准和单应性估计等标准基准测试上达到了竞争性或更优的性能，并且相较于现有方法需要更少的训练数据。