3D Clues Guided Convolution for Depth Completion

Abstract

Depth completion is a task that recovers a dense depth map from a sparse depth map with the corresponding color image. Recently, the intensive depth generation guided by image clues in the color map has achieved good results. Color images can provide structural and semantic information as guidance information, but cannot provide the more important information about geometric relationships. In this paper, we propose a novel network to learn latent 3D cues from RGB images and depth images. More specifically, the network contains a 3D clues extractor and a dense depth generator. The extractor is designed to fusion and extract the 3D joint clues from the color image and sparse depth. The generator is trained with the sparse depth map and 3D clues to producing a more accurate dense depth map. Extensive experiments show that our proposed method has a significant improvement over existing image-guided methods.