Lightweight Self-Supervised Depth Estimation with few-beams LiDAR Data

This paper proposes a lightweight yet effective self-supervised depth completion network trained on monocular videos and sparse raw LiDAR measurements only. Specifically, we utilize a multi-stage network architecture, which depends on cheap CNN layers. We introduce a novel guided sparse convolution operator combining sparse and dense data to extract depth features. To mitigate the impact of outliers commonly present in the sparse raw LiDAR data, we adopt a distance-dependent outlier mask that incorporates an elastic threshold mechanism to selectively discard such points. Our experimental results on the KITTI dataset show the favorable trade-off between accuracy and efficiency achieved by our model, reaching state-of-the-art performance on self-supervised depth estimation from few-beams LiDAR (4-beams), depth completion (64-beams) and a few hundred depth points, using a fraction of the parameters. Our code will be available on https://github.com/franky-ciomp/GSCNN/.