PENet: Towards Precise and Efficient Image Guided Depth Completion

By Mu Hu, Shuling Wang, Bin Li, Shiyu Ning, Linxi Fan, Xiaojin Gong. IEEE International Conference on Robotics and Automation, 2021

384 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2be34bae73c7e41b44d313501a61557d572f24da

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Citations in the corpus

Metric3D v2: A Versatile Monocular Geometric Foundation Model for Zero-Shot Metric Depth and Surface Normal Estimation (2024, positive): “Deep iterative reﬁnement for geometry: Iterative reﬁnement enables multi-step coarse-to-ﬁne prediction and beneﬁts a wide range of geometry estimation tasks, such as optical ﬂow estimation [46], [75], [76], depth completion [43], [77], [78], and stereo matching [45], [79], [80].”
Multimodal Virtual Point 3D Detection (2021, methodology): “We believe more advanced learning based approaches like [18] and [21] may further improve the depth completion and 3D detection performance.”