Abstract
Monocular depth estimation has been increasingly adopted in robotics and autonomous driving for its ability to infer scene geometry from a single camera. In self-supervised monocular depth estimation frameworks, the network jointly generates and exploits depth and pose estimates during training, thereby eliminating the need for depth labels. However, these methods remain challenged by uncertainty in the input data, such as low-texture or dynamic regions, which can cause reduced depth accuracy. To address this, we introduce UM-Depth, a framework that combines motion- and uncertainty-aware refinement to enhance depth accuracy at dynamic object boundaries and in textureless regions. Specifically, we develop a teacherstudent training strategy that embeds uncertainty estimation into both the training pipeline and network architecture, thereby strengthening supervision where photometric signals are weak. Unlike prior motion-aware approaches that incur inference-time overhead and rely on additional labels or auxiliary networks for real-time generation, our method uses optical flow exclusively within the teacher network during training, which eliminating extra labeling demands and any runtime cost. Extensive experiments on the KITTI and Cityscapes datasets demonstrate the effectiveness of our uncertainty-aware refinement. Overall, UM-Depth achieves state-of-the-art results in both self-supervised depth and pose estimation on the KITTI datasets.
Abstract (translated)
单目深度估计因其从单一摄像头中推断场景几何结构的能力，已被机器人技术和自主驾驶领域广泛采用。在自监督的单目深度估计框架中，网络会同时生成和利用深度及姿态估算值来进行训练，从而无需依赖深度标签。然而，这些方法仍然受到输入数据不确定性的影响，如低纹理或动态区域会导致深度准确性降低的问题。为了解决这些问题，我们引入了UM-Depth框架，该框架结合了运动感知与不确定度感知的精炼技术，以提高在动态物体边界和无纹理区域中的深度准确性。 具体而言，我们开发了一种师生训练策略，将不确定性估算嵌入到训练流程及网络架构中，从而增强了光度信号较弱时的监督效果。不同于之前的基于运动的方法，在推理阶段会带来额外的计算开销，并依赖于辅助标签或网络进行实时生成，我们的方法仅在教师网络的训练过程中使用光学流技术，因此不需要额外标注和运行时成本。 我们在KITTI和Cityscapes数据集上进行了广泛实验，结果表明了我们不确定度感知精炼的有效性。总体而言，在无监督深度估计及姿态估计方面，UM-Depth在KITTI数据集中取得了最先进的性能。
URL
https://arxiv.org/abs/2509.13713
