No significant work has been done to directly merge two partially overlapping
scenes using NeRF representations. Given pre-trained NeRF models of a 3D scene
with partial overlapping, this paper aligns them with a rigid transform, by
generalizing the traditional registration pipeline, that is, key point
detection and point set registration, to operate on 3D density fields. To
describe corner points as key points in 3D, we propose to use universal
pre-trained descriptor-generating neural networks that can be trained and
tested on different scenes. We perform experiments to demonstrate that the
descriptor networks can be conveniently trained using a contrastive learning
strategy. We demonstrate that our method, as a global approach, can effectively
register NeRF models, thus making possible future large-scale NeRF construction
by registering its smaller and overlapping NeRFs captured individually.

本文提出了一种使用预训练的 NeRF 模型对 3D 场景进行对齐的方法，该方法通过将传统的关键点检测和点集对齐的流程应用于 3D 密度场来实现。为了在 3D 中描述角点作为关键点，我们建议使用通用的预训练描述符生成神经网络。通过对比学习策略，可以方便地训练描述符网络。我们的方法作为全局方法可以有效地注册 NeRF 模型，从而使未来的大规模 NeRF 构建成为可能。