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Most 3D instance segmentation methods exploit a bottom-up strategy, typicallyincluding resource-exhaustive post-processing. For point grouping, bottom-upmethods rely on prior assumptions about the objects in the form ofhyperparameters, which are domain-specific and need to be carefully tuned. Onthe contrary, we address 3D instance segmentation with a TD3D: the pioneeringcluster-free, fully-convolutional and entirely data-driven approach trained inan end-to-end manner. This is the first top-down method outperforming bottom-upapproaches in 3D domain. With its straightforward pipeline, it demonstratesoutstanding accuracy and generalization ability on the standard indoorbenchmarks: ScanNet v2, its extension ScanNet200, and S3DIS, as well as on theaerial STPLS3D dataset. Besides, our method is much faster on inference thanthe current state-of-the-art grouping-based approaches: our flagshipmodification is 1.9x faster than the most accurate bottom-up method, whilebeing more accurate, and our faster modification shows state-of-the-artaccuracy running at 2.6x speed. Code is available athttps://github.com/SamsungLabs/td3d .

Top-Down Beats Bottom-Up in 3D Instance Segmentation