PENet: Towards Precise and Efficient Image Guided Depth Completion

TL;DR

PENet introduces a dual-branch architecture with geometric encoding and efficient refinement for precise, fast image-guided depth completion, achieving top performance on KITTI benchmark.

Contribution

The paper presents a novel two-branch backbone with geometric convolutional layers and an accelerated refinement method for improved depth completion.

Findings

Achieved 1st place on KITTI leaderboard

Significantly faster inference compared to top methods

Effective fusion of color and depth modalities

Abstract

Image guided depth completion is the task of generating a dense depth map from a sparse depth map and a high quality image. In this task, how to fuse the color and depth modalities plays an important role in achieving good performance. This paper proposes a two-branch backbone that consists of a color-dominant branch and a depth-dominant branch to exploit and fuse two modalities thoroughly. More specifically, one branch inputs a color image and a sparse depth map to predict a dense depth map. The other branch takes as inputs the sparse depth map and the previously predicted depth map, and outputs a dense depth map as well. The depth maps predicted from two branches are complimentary to each other and therefore they are adaptively fused. In addition, we also propose a simple geometric convolutional layer to encode 3D geometric cues. The geometric encoded backbone conducts the fusion of different modalities at multiple stages, leading to good depth completion results. We further implement a dilated and accelerated CSPN++ to refine the fused depth map efficiently. The proposed full model ranks 1st in the KITTI depth completion online leaderboard at the time of submission. It also infers much faster than most of the top ranked methods. The code of this work is available at https://github.com/JUGGHM/PENet_ICRA2021.