ATSGPN: Adaptive Threshold Instance Segmentation Network in 3D Point Cloud

We introduce an adaptive threshold instance segmentation network in point cloud based on similarity group proposal network(SGPN), named adaptive threshold similarity group proposal network(ATSGPN). SGPN learns the feature of point cloud to process similarity matrix and clusters. In our experiments, we find that we cannot always get the proper threshold by heuristic method to divide the points although the similarity matrix is good enough. Based on this idea, we introduce the Threshold Map to learn segmentation threshold. We also improve the feature extraction using edge convolution(EdgeConv). The point cloud first passes EdgeConv to extract features and learns the similarity matrix in feature space. The semantic label of each point and the segmentation threshold can help to generate groups and then calculates confidence to evaluate the group quality and backpropagation. ATSGPN has higher accuracy on Stanford Large-Scale 3D Indoor Spaces Dataset (S3 SID) and fewer steps than SGPN, and there are some experiments can be shown in the paper for its good performance.