動物体の運動と遮蔽を分離する教師なし多フレーム単眼深度推定
Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth
動的シーンでの物体運動による不一致と遮蔽問題を、予測と損失の両レベルで解決するDynamicDepthを提案し、CityscapesとKITTIで大幅な精度向上を達成した。
著者: Ziyue Feng, Liang Yang, Longlong Jing, Haiyan Wang, YingLi Tian, Bing Li
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions. Existing dynamic-object-focused methods only partially solved the mismatch problem at the training loss level. In this paper, we accordingly propose a novel multi-frame monocular depth prediction method to solve these problems at both the prediction and supervision loss levels. Our method, called DynamicDepth, is a new framework trained via a self-supervised cycle consistent learning scheme. A Dynamic Object Motion Disentanglement (DOMD) module is proposed to disentangle object motions to solve the mismatch problem. Moreover, novel occlusion-aware Cost Volume and Re-projection Loss are designed to alleviate the occlusion effects of object motions. Extensive analyses and experiments on the Cityscapes and KITTI datasets show that our method significantly outperforms the state-of-the-art monocular depth prediction methods, especially in the areas of dynamic objects. Code is available at https://github.com/AutoAILab/DynamicDepth
