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.

该研究论文提出了一种全卷积 3D 点云实例分割方法，采用逐点预测方式，避免聚类方法中的任务依赖问题，并使用 Optimal Transport 方法根据动态匹配成本为每个采样点分配目标掩模，显著提升了场景的分割精度，成为比其他方法更简单灵活的 3D 实例分割框架。