  Most 3D instance segmentation methods exploit a bottom-up strategy, typically
including resource-exhaustive post-processing. For point grouping, bottom-up
methods rely on prior assumptions about the objects in the form of
hyperparameters, which are domain-specific and need to be carefully tuned. On
the contrary, we address 3D instance segmentation with a TD3D: the pioneering
cluster-free, fully-convolutional and entirely data-driven approach trained in
an end-to-end manner. This is the first top-down method outperforming bottom-up
approaches in 3D domain. With its straightforward pipeline, it demonstrates
outstanding accuracy and generalization ability on the standard indoor
benchmarks: ScanNet v2, its extension ScanNet200, and S3DIS, as well as on the
aerial STPLS3D dataset. Besides, our method is much faster on inference than
the current state-of-the-art grouping-based approaches: our flagship
modification is 1.9x faster than the most accurate bottom-up method, while
being more accurate, and our faster modification shows state-of-the-art
accuracy running at 2.6x speed. Code is available at
https://github.com/SamsungLabs/td3d .
