Comments (2)
from digital_image_processing.
In this paper, we introduce DenseLivox, a dataset with dense and accurate depth as ground truth. To our best knowledge, it is the first dataset with dense ground truth designed for LiDAR depth completion using a low-cost LiDAR. Also, we develop a simple yet effective multi-task learning network to tackle the problem of depth completion. Compared to the works in the literature, our model’s uniqueness is that it completes a depth map, a normal map, and a grayscale image simultaneously. To address the area with heavy noises, we use modified Huber loss to smooth these outliers’ effect. We evaluate our method on DenseLivox and show that accuracy is greatly improved with the grayscale and normal guidance. Our method outperforms other depth-only methods and is comparable to the methods that take RGB and depth as input.
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
- From Depth What Can You See? Depth Completion via Auxiliary Image Reconstruction HOT 2
- Sparsity invariant cnns HOT 2
- Dynamic Spatial Propagation Network for Depth Completion HOT 3
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- 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.