Confidence Guided Depth Completion Network
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Abstract (arXiv)
The paper proposes an image-guided depth completion method to estimate accurate dense depth maps with fast computation time. The proposed network has two-stage structure. The first stage predicts a first depth map. Then, the second stage further refines the first depth map using the confidence maps. The second stage consists of two layers, each of which focuses on different regions and generates a refined depth map and a confidence map. The final depth map is obtained by combining two depth maps from the second stage using the corresponding confidence maps. Compared with the top-ranked models on the KITTI depth completion online leaderboard, the proposed model shows much faster computation time and competitive performance.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 16 references that are in the map. First posted on arXiv 7 Feb 2022.
Builds on
11 of 16 references.
References · 11
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2017Sparsity Invariant CNNscited by 326
- 2021PENet: Towards Precise and Efficient Image Guided Depth Completioncited by 146
- 2018Learning Depth with Convolutional Spatial Propagation Networkcited by 136
- 2018DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Imagecited by 129
- 2019Learning Guided Convolutional Network for Depth Completioncited by 111
- 2019CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completioncited by 110
- 2019Depth Completion from Sparse LiDAR Data with Depth-Normal Constraintscited by 108
- 2019Sparse and noisy LiDAR completion with RGB guidance and uncertaintycited by 79
- 2020Adaptive Context-Aware Multi-Modal Network for Depth Completioncited by 69
- 2021Depth Completion using Plane-Residual Representationcited by 24
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