Paper ID: 2403.05895

DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and Depth from Monocular Videos

Xiuzhe Wu, Xiaoyang Lyu, Qihao Huang, Yong Liu, Yang Wu, Ying Shan, Xiaojuan Qi

Although considerable advancements have been attained in self-supervised depth estimation from monocular videos, most existing methods often treat all objects in a video as static entities, which however violates the dynamic nature of real-world scenes and fails to model the geometry and motion of moving objects. In this paper, we propose a self-supervised method to jointly learn 3D motion and depth from monocular videos. Our system contains a depth estimation module to predict depth, and a new decomposed object-wise 3D motion (DO3D) estimation module to predict ego-motion and 3D object motion. Depth and motion networks work collaboratively to faithfully model the geometry and dynamics of real-world scenes, which, in turn, benefits both depth and 3D motion estimation. Their predictions are further combined to synthesize a novel video frame for self-supervised training. As a core component of our framework, DO3D is a new motion disentanglement module that learns to predict camera ego-motion and instance-aware 3D object motion separately. To alleviate the difficulties in estimating non-rigid 3D object motions, they are decomposed to object-wise 6-DoF global transformations and a pixel-wise local 3D motion deformation field. Qualitative and quantitative experiments are conducted on three benchmark datasets, including KITTI, Cityscapes, and VKITTI2, where our model delivers superior performance in all evaluated settings. For the depth estimation task, our model outperforms all compared research works in the high-resolution setting, attaining an absolute relative depth error (abs rel) of 0.099 on the KITTI benchmark. Besides, our optical flow estimation results (an overall EPE of 7.09 on KITTI) also surpass state-of-the-art methods and largely improve the estimation of dynamic regions, demonstrating the effectiveness of our motion model. Our code will be available.

Submitted: Mar 9, 2024