Instance segmentation is an important pre-processing task in numerous real-world applications, such as robotics, autonomous vehicles, and human-computer interaction. However, there has been little research on 3D point cloud instance segmentation of bin-picking scenes in which multiple objects of the same class are stacked together. Compared with the rapid development of deep learning for two-dimensional (2D) image tasks, deep learning-based 3D point cloud segmentation still has a lot of room for development. In such a situation, distinguishing a large number of occluded objects of the same class is a highly challenging problem. In a usual bin-picking scene, an object model is known and the number of object type is one. Thus, the semantic information can be ignored; instead, the focus is put on the segmentation of instances. Based on this task requirement, we propose a network (FPCC-Net) that infers feature centers of each instance and then clusters the remaining points to the closest feature center in feature embedding space. FPCC-Net includes two subnets, one for inferring the feature centers for clustering and the other for describing features of each point. The proposed method is compared with existing 3D point cloud and 2D segmentation methods in some bin-picking scenes. It is shown that FPCC-Net improves average precision (AP) by about 40\% than SGPN and can process about 60,000 points in about 0.8 [s].
翻译:在很多现实应用中,例如机器人、自主飞行器和人机互动,对各事物进行早期处理是一项重要的任务。然而,对于三维点云层分解,即将同一类的多个对象堆叠在一起的宾式挑选场景的三维点云层分解,研究很少。与两维(2D)图像任务的深度学习相比,深学习基三维点云分解仍然有很大的发展空间。在这种情况下,区分同一类的大量隐蔽对象是一个极具挑战性的问题。在一个通常的垃圾桶场景中,一个对象模型是已知的,对象类型的数目是一。因此,语义信息可以被忽略;相反,重点是对实例的分解。根据这一任务要求,我们建议建立一个网络(FCC-Net),将每个实例的特征中心集中起来,然后将其余的点集中到最接近的特征嵌入空间中。FPC-Net包含两个子网,一个是用来推断60-型组群集的地标中心,一个是关于60-型集的地标中心,另一个是用来描述目前2个点的平流路段。我们建议采用的网络-B-方法可以用来比较目前G-B-C-SQ-SQ-S-S-S-SQ-S-SQ-S-C-SQ-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx