Depth estimation aims to predict dense depth maps. In autonomous driving scenes, sparsity of annotations makes the task challenging. Supervised models produce concave objects due to insufficient structural information. They overfit to valid pixels and fail to restore spatial structures. Self-supervised methods are proposed for the problem. Their robustness is limited by pose estimation, leading to erroneous results in natural scenes. In this paper, we propose a supervised framework termed Diffusion-Augmented Depth Prediction (DADP). We leverage the structural characteristics of diffusion model to enforce depth structures of depth models in a plug-and-play manner. An object-guided integrality loss is also proposed to further enhance regional structure integrality by fetching objective information. We evaluate DADP on three driving benchmarks and achieve significant improvements in depth structures and robustness. Our work provides a new perspective on depth estimation with sparse annotations in autonomous driving scenes.

翻译：深度估计旨在预测密集的深度图。在自动驾驶场景中，标注的稀疏性使得该任务充满挑战。由于缺乏足够的结构信息，监督模型会产生凹陷的物体。它们过度拟合有效像素，无法恢复空间结构。针对该问题，人们提出了自监督方法，但其鲁棒性受位姿估计限制，导致在自然场景中出现错误结果。本文提出了一种称为扩散增强深度预测（DADP）的监督框架。我们利用扩散模型的结构特性，以即插即用的方式增强深度模型的深度结构。同时，我们还提出了一种目标引导完整性损失，通过获取目标信息进一步增强区域结构完整性。我们在三个驾驶基准上评估了DADP，并在深度结构和鲁棒性方面取得了显著改进。我们的工作为自动驾驶场景中稀疏标注的深度估计提供了新视角。