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Large-scale training data with high-quality annotations is critical fortraining semantic and instance segmentation models. Unfortunately, pixel-wiseannotation is labor-intensive and costly, raising the demand for more efficientlabeling strategies. In this work, we present a novel 3D-to-2D label transfermethod, Panoptic NeRF, which aims for obtaining per-pixel 2D semantic andinstance labels from easy-to-obtain coarse 3D bounding primitives. Our methodutilizes NeRF as a differentiable tool to unify coarse 3D annotations and 2Dsemantic cues transferred from existing datasets. We demonstrate that thiscombination allows for improved geometry guided by semantic information,enabling rendering of accurate semantic maps across multiple views.Furthermore, this fusion process resolves label ambiguity of the coarse 3Dannotations and filters noise in the 2D predictions. By inferring in 3D spaceand rendering to 2D labels, our 2D semantic and instance labels are multi-viewconsistent by design. Experimental results show that Panoptic NeRF outperformsexisting label transfer methods in terms of accuracy and multi-view consistencyon challenging urban scenes of the KITTI-360 dataset.

Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation