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3D semantic scene understanding is a fundamental challenge in computervision. It enables mobile agents to autonomously plan and navigate arbitraryenvironments. SSC formalizes this challenge as jointly estimating densegeometry and semantic information from sparse observations of a scene. Currentmethods for SSC are generally trained on 3D ground truth based on aggregatedLiDAR scans. This process relies on special sensors and annotation by handwhich are costly and do not scale well. To overcome this issue, our workpresents the first self-supervised approach to SSC called S4C that does notrely on 3D ground truth data. Our proposed method can reconstruct a scene froma single image and only relies on videos and pseudo segmentation ground truthgenerated from off-the-shelf image segmentation network during training. Unlikeexisting methods, which use discrete voxel grids, we represent scenes asimplicit semantic fields. This formulation allows querying any point within thecamera frustum for occupancy and semantic class. Our architecture is trainedthrough rendering-based self-supervised losses. Nonetheless, our methodachieves performance close to fully supervised state-of-the-art methods.Additionally, our method demonstrates strong generalization capabilities andcan synthesize accurate segmentation maps for far away viewpoints.

S4C: Self-Supervised Semantic Scene Completion with Neural Fields