Semantic Segmentation of In-Vehicle Point Cloud With Improved RangeNet++ Loss Function

Abstract

To solve the problem of inaccurate object segmentation caused by unbalanced samples for in-vehicle point cloud, an improved semantic segmentation network RangeNet++ based on asymmetric loss function (AsL-RangeNet++) is proposed, which uses asymmetric loss (AsL) function and Adam optimizer to calculate and adjust object weights, achieve optimal point cloud segmentation. AsL-RangeNet++ can solve the problem of unbalance between positive and negative samples and label error in multi-label classification by calculating the weights of positive and negative samples respectively and more accurately segments the point cloud of small targets. A large number of experiments on the widely used SemanticKITTI dataset show that the proposed method has higher segmentation accuracy and better adaptability than the current mainstream methods.