Noise-Aware Unsupervised Deep Lidar-Stereo Fusion
This addresses depth estimation challenges in robotics and autonomous driving by providing a robust fusion method, though it is incremental in improving existing fusion techniques.
The paper tackles the problem of noisy Lidar points and sensor misalignment in Lidar-stereo fusion by introducing LidarStereoNet, an unsupervised network that outperforms state-of-the-art methods in depth estimation without ground truth data.
In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth maps. By introducing a novel "Feedback Loop'' to connect the network input with output, LidarStereoNet could tackle both noisy Lidar points and misalignment between sensors that have been ignored in existing Lidar-stereo fusion studies. Besides, we propose to incorporate a piecewise planar model into network learning to further constrain depths to conform to the underlying 3D geometry. Extensive quantitative and qualitative evaluations on both real and synthetic datasets demonstrate the superiority of our method, which outperforms state-of-the-art stereo matching, depth completion and Lidar-Stereo fusion approaches significantly.