Unsupervised monocular depth estimation techniques have demonstrated
encouraging results but typically assume that the scene is static. These
techniques suffer when trained on dynamical scenes, where apparent object
motion can equally be explained by hypothesizing the object's independent
motion, or by altering its depth. This ambiguity causes depth estimators to
predict erroneous depth for moving objects. To resolve this issue, we introduce
Dynamo-Depth, an unifying approach that disambiguates dynamical motion by
jointly learning monocular depth, 3D independent flow field, and motion
segmentation from unlabeled monocular videos. Specifically, we offer our key
insight that a good initial estimation of motion segmentation is sufficient for
jointly learning depth and independent motion despite the fundamental
underlying ambiguity. Our proposed method achieves state-of-the-art performance
on monocular depth estimation on Waymo Open and nuScenes Dataset with
significant improvement in the depth of moving objects. Code and additional
results are available at this https URL

Dynamo-Depth 是一种基于未标记单目视频筛选的三维独立光流场和运动分割的联合学习的方法，通过解决动态场景下深度估计的不确定性问题，实现了在 Waymo Open 和 nuScenes 数据集上单目深度估计的最新成果，特别在移动物体的深度上有显著改进。