Structure-Aware Radar-Camera Depth Estimation

Abstract

Monocular depth estimation aims to determine the depth of each pixel from an RGB image captured by a monocular camera. The development of deep learning has significantly advanced this field by facilitating the learning of depth features from some well-annotated datasets Geiger_Lenz_Stiller_Urtasun_2013,silberman2012indoor. Eigen et al. eigen2014depth first introduce a multi-scale fusion network for depth regression. Following this, subsequent improvements have come from reinterpreting the regression task as a classification problem bhat2021adabins,Li_Wang_Liu_Jiang_2022, incorporating additional priors shao2023nddepth,yang2023gedepth, and developing more effective objective function xian2020structure,Yin_Liu_Shen_Yan_2019. Despite these advances, generalizing to unseen domains remains a challenge. Recently, several methods have employed affine-invariant loss to enable multi-dataset joint training MiDaS,ZeroDepth,guizilini2023towards,Dany. Among them, Depth Anything Dany has shown leading performance in zero-shot monocular depth estimation. While it struggles to estimate accurate metric depth due to the lack of explicit depth cues, it excels at extracting structural information from unseen images, producing structure-detailed monocular depth.

Tasks

Reproductions