複数マスクを用いた単眼動画からの深度と自己運動の教師なし学習
Unsupervised Learning of Monocular Depth and Ego-Motion Using Multiple Masks
単眼動画から深度と自己運動を教師なしで推定する手法を提案し、隣接フレーム投影時のオクルージョンや空白を複数マスクで除外して学習効率と精度を向上させた。
著者: Guangming Wang, Hesheng Wang, Yiling Liu, Weidong Chen
分類: cs.CV, cs.RO
原文アブストラクト
A new unsupervised learning method of depth and ego-motion using multiple masks from monocular video is proposed in this paper. The depth estimation network and the ego-motion estimation network are trained according to the constraints of depth and ego-motion without truth values. The main contribution of our method is to carefully consider the occlusion of the pixels generated when the adjacent frames are projected to each other, and the blank problem generated in the projection target imaging plane. Two fine masks are designed to solve most of the image pixel mismatch caused by the movement of the camera. In addition, some relatively rare circumstances are considered, and repeated masking is proposed. To some extent, the method is to use a geometric relationship to filter the mismatched pixels for training, making unsupervised learning more efficient and accurate. The experiments on KITTI dataset show our method achieves good performance in terms of depth and ego-motion. The generalization capability of our method is demonstrated by training on the low-quality uncalibrated bike video dataset and evaluating on KITTI dataset, and the results are still good.
