  The current state-of-the-art methods in 3D instance segmentation typically
involve a clustering step, despite the tendency towards heuristics, greedy
algorithms, and a lack of robustness to the changes in data statistics. In
contrast, we propose a fully-convolutional 3D point cloud instance segmentation
method that works in a per-point prediction fashion. In doing so it avoids the
challenges that clustering-based methods face: introducing dependencies among
different tasks of the model. We find the key to its success is assigning a
suitable target to each sampled point. Instead of the commonly used static or
distance-based assignment strategies, we propose to use an Optimal Transport
approach to optimally assign target masks to the sampled points according to
the dynamic matching costs. Our approach achieves promising results on both
ScanNet and S3DIS benchmarks. The proposed approach removes intertask
dependencies and thus represents a simpler and more flexible 3D instance
segmentation framework than other competing methods, while achieving improved
segmentation accuracy.
