LiDAR semantic segmentation with local consistency constrained KPConv LSTM

Abstract

As a fundamental task for autonomous driving, LiDAR point cloud semantic segmentation has been intensively studied in recent years. Despite the great progress, achieving satisfactory semantic segmentation is still very challenging due to the sparsity of LiDAR points and the shape diversity of the classes in the open world. In this paper we propose a local consistency constrained KPConv-LSTM module to benefit the existing methods. It enhances point-based LSTM with several KPConvs to strengthen and align the previous hidden features, thus improving the temporal feature propagation. A temporal weighting block is designed within the module to further reduce the error caused by the misalignment of moving objects. In addition, a special local consistency loss is proposed to encourage the local smoothness of the feature, thereby providing more consistent feature for temporal propagation in LSTM. We apply our method to various existing LiDAR semantic segmentation models. The experimental results on multiple datasets show that our method can produce notable improvements on all of them, validating the effectiveness and generality of the method.