Papers › PENet: Towards Precise and Efficient Image Guided Depth Completion

PENet: Towards Precise and Efficient Image Guided Depth Completion

1 Mar 2021arXiv:2103.00783archive 2025-07-28

Mu Hu, Shuling Wang, Bin Li, Shiyu Ning, Li Fan, Xiaojin Gong

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.

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Code

JUGGHM/PENet_ICRA2021 officialmentioned in papermentioned on GitHubpytorch report
alexsunnik/redc mentioned on GitHubpytorch report

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Depth Completion

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Depth Completion KITTI Depth Completion PENet MAE 210.55 #3 of 16 Archive leaderboard report
Depth Completion KITTI Depth Completion PENet RMSE 730.08 #3 of 16 Archive leaderboard report
Depth Completion KITTI Depth Completion PENet Runtime [ms] 32 #3 of 16 Archive leaderboard report
Depth Completion KITTI Depth Completion PENet iMAE 0.94 #3 of 16 Archive leaderboard report
Depth Completion KITTI Depth Completion PENet iRMSE 2.17 #3 of 16 Archive leaderboard report

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