论文标题
点组：3D实例分段的双集点分组
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
论文作者
论文摘要
实例细分是场景理解的重要任务。与完全发达的2D相比，点云的3D实例细分有很大的改进空间。在本文中，我们提出了一种新的端到端自下而上架构，专门针对通过探索对象之间的空间空间来更好地分组点。我们设计了一个两个分支网络，以提取点特征并预测语义标签和偏移，以将每个点转向其各自的实例质心。遵循聚类组件，以利用它们的互补强度同时利用原始和偏移偏移的点坐标集。此外，我们制定了SCORENET来评估候选实例，然后进行非最大抑制（NMS）以去除重复项。我们对两个具有挑战性的数据集进行了广泛的实验，即Scannet V2和S3DIS，我们的方法在其上实现了最高的性能，63.6％和64.0％，而前者最佳解决方案则以IOU阈值0.5的地图获得了54.9％和54.4％。
Instance segmentation is an important task for scene understanding. Compared to the fully-developed 2D, 3D instance segmentation for point clouds have much room to improve. In this paper, we present PointGroup, a new end-to-end bottom-up architecture, specifically focused on better grouping the points by exploring the void space between objects. We design a two-branch network to extract point features and predict semantic labels and offsets, for shifting each point towards its respective instance centroid. A clustering component is followed to utilize both the original and offset-shifted point coordinate sets, taking advantage of their complementary strength. Further, we formulate the ScoreNet to evaluate the candidate instances, followed by the Non-Maximum Suppression (NMS) to remove duplicates. We conduct extensive experiments on two challenging datasets, ScanNet v2 and S3DIS, on which our method achieves the highest performance, 63.6% and 64.0%, compared to 54.9% and 54.4% achieved by former best solutions in terms of mAP with IoU threshold 0.5.
