Sparse and Noisy LiDAR Completion with RGB Guidance and Uncertainty
| Autor: | Wouter Van Gansbeke, Bert De Brabandere, Luc Van Gool, Davy Neven |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Monocular Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Point cloud Robotics Stereoscopy 02 engineering and technology law.invention 03 medical and health sciences 0302 clinical medicine Lidar law 030221 ophthalmology & optometry 0202 electrical engineering electronic engineering information engineering RGB color model Leverage (statistics) 020201 artificial intelligence & image processing Computer vision Artificial intelligence Depth perception business |
| Zdroj: | MVA |
| DOI: | 10.23919/mva.2019.8757939 |
| Popis: | This work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions. A multitude of applications depend on the awareness of their surroundings, and use depth cues to reason and react accordingly. On the one hand, monocular depth prediction methods fail to generate absolute and precise depth maps. On the other hand, stereoscopic approaches are still significantly outperformed by LiDAR based approaches. The goal of the depth completion task is to generate dense depth predictions from sparse and irregular point clouds which are mapped to a 2D plane. We propose a new framework which extracts both global and local information in order to produce proper depth maps. We argue that simple depth completion does not require a deep network. However, we additionally propose a fusion method with RGB guidance from a monocular camera in order to leverage object information and to correct mistakes in the sparse input. This improves the accuracy significantly. Moreover, confidence masks are exploited in order to take into account the uncertainty in the depth predictions from each modality. This fusion method outperforms the state-of-the-art and ranks first on the KITTI depth completion benchmark. Our code with visualizations is available. Comment: 7 pages, 3 figures |
| Databáze: | OpenAIRE |
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