1 research outputs found
Monocular Depth Prediction through Continuous 3D Loss
This paper reports a new continuous 3D loss function for learning depth from
monocular images. The dense depth prediction from a monocular image is
supervised using sparse LIDAR points, which enables us to leverage available
open source datasets with camera-LIDAR sensor suites during training.
Currently, accurate and affordable range sensor is not readily available.
Stereo cameras and LIDARs measure depth either inaccurately or sparsely/costly.
In contrast to the current point-to-point loss evaluation approach, the
proposed 3D loss treats point clouds as continuous objects; therefore, it
compensates for the lack of dense ground truth depth due to LIDAR's sparsity
measurements. We applied the proposed loss in three state-of-the-art monocular
depth prediction approaches DORN, BTS, and Monodepth2. Experimental evaluation
shows that the proposed loss improves the depth prediction accuracy and
produces point-clouds with more consistent 3D geometric structures compared
with all tested baselines, implying the benefit of the proposed loss on general
depth prediction networks. A video demo of this work is available at
https://youtu.be/5HL8BjSAY4Y.Comment: 8 pages, 4 figures. Accepted by IROS 202