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SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

Abstract

In this paper, we introduce SalsaNext for the uncertainty-aware semantic segmentation of a full 3D LiDAR point cloud in real-time. SalsaNext is the next version of SalsaNet 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 introduce a new context module, replace the ResNet encoder blocks with a new residual dilated convolution stack with gradually increasing receptive fields and add the pixel-shuffle layer in the decoder. Additionally, we 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 . We finally inject a Bayesian treatment to compute the epistemic and aleatoric uncertainties for each point in the cloud. We provide a thorough quantitative evaluation on the Semantic-KITTI dataset, which demonstrates that the proposed SalsaNext outperforms other state-of-the-art semantic segmentation.

Examples

Example Gif

Video

Inference of Sequence 13

Semantic Kitti Segmentation Scores

The up-to-date scores can be found in the Semantic-Kitti page.

How to use the code

First create the anaconda env with: conda env create -f salsanext_cuda10.yml --name salsanext then activate the environment with conda activate salsanext.

To train/eval you can use the following scripts:

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Pretrained Model

SalsaNext

Disclamer

We based our code on RangeNet++, please go show some support!

Citation

@misc{cortinhal2020salsanext,
    title={SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving},
    author={Tiago Cortinhal and George Tzelepis and Eren Erdal Aksoy},
    year={2020},
    eprint={2003.03653},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}