Comments (2)
from digital_image_processing.
Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving. However, depth completion faces 3 main challenges: the irregularly spaced pattern in the sparse depth input, the difficulty in handling multiple sensor modalities (when color images are available), as well as the lack of dense, pixel-level ground truth depth labels for training. In this work, we address all these challenges. Specifically, we develop a deep regression model to learn a direct mapping from sparse depth (and color images) input to dense depth prediction. We also propose a self-supervised training framework that requires only sequences of color and sparse depth images, without the need for dense depth labels. Our experiments demonstrate that the self-supervised framework outperforms a number of existing solutions trained with semi-dense annotations. Furthermore, when trained with semi-dense annotations, our network attains state-of-the-art accuracy and is the winning approach on the KITTI depth completion benchmark at the time of submission.
from digital_image_processing.
Related Issues (15)
- ICCV 2019 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
- From Depth What Can You See? Depth Completion via Auxiliary Image Reconstruction 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.