论文标题
部分可观测时空混沌系统的无模型预测
Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise Binarization
论文作者
论文摘要
点云上的实例分割对于3D场景的理解至关重要。大多数SOTA都采用距离聚类，这通常是有效的，但在用相同的语义标签（尤其是在共享相邻点）的相邻对象中表现不佳。由于偏移点的分布不均匀，这些现有方法几乎不能集中所有实例点。为此，我们设计了一种名为PBNET的新颖的分裂和诱使策略，该策略将每个点二进制并分别簇为细分实例。我们的二进制聚类将偏移实例点分为两类：高密度和低密度点（HPS vs. LPS）。可以通过删除LP清楚地分离相邻的对象，然后通过通过邻居投票方法分配有限元来完成和完善。为了抑制潜在的过度细分，我们建议为每个实例构建用重量掩码构建本地场景。作为插件，提议的二进制聚类可以替代传统的距离聚类，并在许多主流基线上带来一致的性能增长。 ScannETV2和S3DIS数据集的一系列实验表明了我们的模型的优势。特别是，PBNET在ScannETV2官方基准挑战中排名第一，获得了最高地图。代码将在https://github.com/weiguangzhao/pbnet上公开提供。
Instance segmentation on point clouds is crucially important for 3D scene understanding. Most SOTAs adopt distance clustering, which is typically effective but does not perform well in segmenting adjacent objects with the same semantic label (especially when they share neighboring points). Due to the uneven distribution of offset points, these existing methods can hardly cluster all instance points. To this end, we design a novel divide-and-conquer strategy named PBNet that binarizes each point and clusters them separately to segment instances. Our binary clustering divides offset instance points into two categories: high and low density points (HPs vs. LPs). Adjacent objects can be clearly separated by removing LPs, and then be completed and refined by assigning LPs via a neighbor voting method. To suppress potential over-segmentation, we propose to construct local scenes with the weight mask for each instance. As a plug-in, the proposed binary clustering can replace traditional distance clustering and lead to consistent performance gains on many mainstream baselines. A series of experiments on ScanNetV2 and S3DIS datasets indicate the superiority of our model. In particular, PBNet ranks first on the ScanNetV2 official benchmark challenge, achieving the highest mAP. Code will be available publicly at https://github.com/weiguangzhao/PBNet.
