Diffusion-Augmented Depth Prediction with Sparse Annotations
Jiaqi Li Yiran Wang Zihao Huang Jinghong Zheng Ke Xian Zhiguo Cao Jianming Zhang
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.