Comments (2)
from digital_image_processing.
Modern high-definition LIDAR is expensive for commercial autonomous driving vehicles and small indoor robots. An affordable solution to this problem is fusion of planar LIDAR with RGB images to provide a similar level of perception capability. Even though state-of-the-art methods provide approaches to predict depth information from limited sensor input, they are usually a simple concatenation of sparse LIDAR features and dense RGB features through an end-to-end fusion architecture. In this paper, we introduce an inductive late-fusion block which better fuses different sensor modalities inspired by a probability model. The proposed demonstration and aggregation network propagates the mixed context and depth features to the prediction network and serves as a prior knowledge of the depth completion. This late-fusion block uses the dense context features to guide the depth prediction based on demonstrations by sparse depth features. In addition to evaluating the proposed method on benchmark depth completion datasets including NYUDepthV2 and KITTI, we also test the proposed method on a simulated planar LIDAR dataset. Our method shows promising results compared to previous approaches on both the benchmark datasets and simulated dataset with various 3D densities.
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
- 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.