LSMD-Net: LiDAR-Stereo Fusion with Mixture Density Network for Depth Sensing

HanXi Yin, Lei Deng, Zhixiang Chen, Baohua Chen, Ting Sun, Xie Yusen, Junewei Xiao, Yeyu Fu, Shuixin Deng, Xiu Li; Proceedings of the Asian Conference on Computer Vision (ACCV), 2022, pp. 552-568


Depth sensing is critical to many computer vision applications but remains challenge to generate accurate dense information with single type sensor. The stereo camera sensor can provide dense depth prediction but underperforms in texture-less, repetitive and occlusion areas while the LiDAR sensor can generate accurate measurements but results in sparse map. In this paper, we advocate to fuse LiDAR and stereo camera for accurate dense depth sensing. We consider the fusion of multiple sensors as a multimodal prediction problem. We propose a novel end-to-end learning framework, dubbed as LSMD-Net to faithfully generate dense depth. The proposed method has dual-branch disparity predictor and predicts a bimodal Laplacian distribution over disparity at each pixel. This distribution has two modes which captures the information from two branches. Predictions from the branch with higher confidence is selected as the final disparity result at each specific pixel. Our fusion method can be applied for different type of LiDARs. Besides the existing dataset captured by conventional spinning LiDAR, we build a multiple sensor system with a non-repeating scanning LiDAR and a stereo camera and construct a depth prediction dataset with this system. Evaluations on both KITTI datasets and our home-made dataset demonstrate the superiority of our proposed method in terms of accuracy and computation time.

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@InProceedings{Yin_2022_ACCV, author = {Yin, HanXi and Deng, Lei and Chen, Zhixiang and Chen, Baohua and Sun, Ting and Yusen, Xie and Xiao, Junewei and Fu, Yeyu and Deng, Shuixin and Li, Xiu}, title = {LSMD-Net: LiDAR-Stereo Fusion with Mixture Density Network for Depth Sensing}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {December}, year = {2022}, pages = {552-568} }