ScaleDepth: Decomposing Metric Depth Estimation into Semantic-aware Scale Prediction and Adaptive Relative Depth Estimation

Estimating the depth map of an image in the wild is a challenging visual task. Compared to relative depth estimation, metric depth estimation attracts more attention due to its practical physical significance and critical applications in real-life scenarios. However, existing depth estimation methods typically focus only on generalization of relative depth, neglecting the importance of metric depth generalization. To address this challenge, we propose a novel monocular depth estimation method called ScaleDepth. It decomposes metric depth into scene scale and relative depth and predicts them through a semantic-aware scale prediction (SASP) module and an adaptive relative depth estimation (ARDE) module,…