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arXiv · 2310.05133

Geometry Aware Field-to-field Transformations for 3D Semantic Segmentation

Dominik Hollidt·Clinton Wang·Polina Golland·Marc Pollefeys
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Abstract

We present a novel approach to perform 3D semantic segmentation solely from 2D supervision by leveraging Neural Radiance Fields (NeRFs). By extracting features along a surface point cloud, we achieve a compact representation of the scene which is sample-efficient and conducive to 3D reasoning. Learning this feature space in an unsupervised manner via masked autoencoding enables few-shot segmentation. Our method is agnostic to the scene parameterization, working on scenes fit with any type of NeRF.

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Dominik Hollidt, Clinton Wang, Polina Golland, Marc Pollefeys. 2023-10-08. Geometry Aware Field-to-field Transformations for 3D Semantic Segmentation. https://arxiv.org/abs/2310.05133

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