SalsaNext: Fast Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
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In this paper, we introduce SalsaNext for the semantic segmentation of a full 3D LiDAR point cloud in real-time. SalsaNext is the next version of SalsaNet [1] which has an encoder-decoder architecture where the encoder unit has a set of ResNet blocks and the decoder part combines upsampled features from the residual blocks. In contrast to SalsaNet, we have an additional layer in the encoder and decoder, introduce the context module, switch from stride convolution to average pooling and also apply central dropout treatment. To directly optimize the Jaccard index, we further combine the weighted cross-entropy loss with Lovasz-Softmax loss [2]. We provide a thorough quantitative evaluation on the Semantic-KITTI dataset [3], which demonstrates that the proposed SalsaNext outperforms other state-of-the-art semantic segmentation networks in terms of accuracy and computation time. We also release our source code https://github.com/TiagoCortinhal/SalsaNext.
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