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We propose a weakly supervised semantic segmentation method for point cloudsthat predicts "per-point" labels from just "whole-scene" annotations whileachieving the performance of recent fully supervised approaches. Our core ideais to propagate the scene-level labels to each point in the point cloud bycreating pseudo labels in a conservative way. Specifically, we over-segmentpoint cloud features via unsupervised clustering and associate scene-levellabels with clusters through bipartite matching, thus propagating scene labelsonly to the most relevant clusters, leaving the rest to be guided solely viaunsupervised clustering. We empirically demonstrate that over-segmentation andbipartite assignment plays a crucial role. We evaluate our method on ScanNetand S3DIS datasets, outperforming state of the art, and demonstrate that we canachieve results comparable to fully supervised methods.

Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly Supervised Point Cloud Segmentation