G-Depth: An Efficient Graph Method for Robust Depth Completion

Abstract

Depth completion has played a vital role in enhancing depth perception in various applications such as autonomous driving, robotics, and 3D reconstruction. It is a critical task that involves generating a dense depth map from an RGB image and an aligned sparse depth map. However, many existing studies simply assume the depth map to be readily available, overlooking the fact that depth maps are often missing or subject to perturbations in real-world scenarios. The absence of these data can mislead the network into overfitting incorrect patterns, ultimately diminishing the performance of depth completion models. To tackle these challenges, we propose G-Depth, a method that innovatively involves Graph Neural Networks (GNNs) into a two-branch backbone. By integrating the graph convolutional mechanism into the deep layers of the network, G-Depth achieves an expanded receptive field, enabling a more accurate understanding and capture of spatial relationships between objects. To enhance robustness, we also propose a novel Enhanced Data Distillation(EDD) method to address the issue of modality absence, mitigating its negative effects, achieving a 73.2% average improvement in RMSE without sparse depth input. Comprehensive experiments on KITTI dataset demonstrate the effectiveness and robustness of the proposed G-Depth.