Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation
This addresses computational limitations for real-world applications like self-driving cars and robots, though it appears incremental.
The paper tackles the problem of expensive 4D cost volumes in deep stereo matching by introducing sparse LiDAR points into iterative disparity updates and using self-supervised training, achieving comparable results with related methods.
Deep stereo matching has made significant progress in recent years. However, state-of-the-art methods are based on expensive 4D cost volume, which limits their use in real-world applications. To address this issue, 3D correlation maps and iterative disparity updates have been proposed. Regarding that in real-world platforms, such as self-driving cars and robots, the Lidar is usually installed. Thus we further introduce the sparse Lidar point into the iterative updates, which alleviates the burden of network updating the disparity from zero states. Furthermore, we propose training the network in a self-supervised way so that it can be trained on any captured data for better generalization ability. Experiments and comparisons show that the presented method is effective and achieves comparable results with related methods.