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No significant work has been done to directly merge two partially overlappingscenes using NeRF representations. Given pre-trained NeRF models of a 3D scenewith partial overlapping, this paper aligns them with a rigid transform, bygeneralizing the traditional registration pipeline, that is, key pointdetection and point set registration, to operate on 3D density fields. Todescribe corner points as key points in 3D, we propose to use universalpre-trained descriptor-generating neural networks that can be trained andtested on different scenes. We perform experiments to demonstrate that thedescriptor networks can be conveniently trained using a contrastive learningstrategy. We demonstrate that our method, as a global approach, can effectivelyregister NeRF models, thus making possible future large-scale NeRF constructionby registering its smaller and overlapping NeRFs captured individually.

Registering Neural Radiance Fields as 3D Density Images