Comments (2)
from digital_image_processing.
Depth completion recovers dense depth from sparse measurements, e.g., LiDAR. Existing depth-only methods use sparse depth as the only input. However, these methods may fail to recover semantics consistent boundaries, or small/thin objects due to 1) the sparse nature of depth points and 2) the lack of images to provide semantic cues. This paper continues this line of research and aims to overcome the above shortcomings. The unique design of our depth completion model is that it simultaneously outputs a reconstructed image and a dense depth map. Specifically, we formulate image reconstruction from sparse depth as an auxiliary task during training that is supervised by the unlabelled gray-scale images. Our design allows the depth completion network to learn complementary image features that help to better understand object structures. The extra supervision incurred by image reconstruction is minimal, because no annotations other than the image are needed. We evaluate our method on the KITTI depth completion benchmark and show that depth completion can be significantly improved via the auxiliary supervision of image reconstruction. Our algorithm consistently outperforms depth-only methods and is also effective for indoor scenes like NYUv2.
from digital_image_processing.
Related Issues (15)
- ICCV 2019 HOT 2
- Self-Supervised Sparse-to-Dense: Self-Supervised Depth Completion from LiDAR and Monocular Camera HOT 2
- Sparse and Noisy LiDAR Completion with RGB Guidance and Uncertainty HOT 1
- LIDAR and Monocular Camera Fusion: On-road Depth Completion for Autonomous Driving HOT 2
- Deep Adaptive LiDAR: End-to-end Optimization of Sampling and Depth Completion at Low Sampling Rates HOT 2
- UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion HOT 2
- Radar-Camera Pixel Depth Association for Depth Completion HOT 2
- Depth Completion via Inductive Fusion of Planar LIDAR and Monocular Camera HOT 2
- Grayscale And Normal Guided Depth Completion With A Low-Cost Lidar HOT 2
- Sparsity invariant cnns HOT 2
- Dynamic Spatial Propagation Network for Depth Completion HOT 3
- A comparative review of plausible hole filling strategies in the context of scene depth image completion HOT 2
- A survey on deep learning techniques for stereo-based depth estimation HOT 2
- Depth map artefacts reduction: a review HOT 2
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from digital_image_processing.