Deep Reinforcement Learning of Volume-guided Progressive View Inpainting for 3D Point Scene Completion from a Single Depth Image
| Autor: | Dong Du, Pan Pan, Ligang Liu, Xin Yang, Mingdai Yang, Xiaoguang Han, Zixiang Xiong, Jingming Yu, Shuguang Cui, Zhaoxuan Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Deep learning Point cloud Inpainting Volume (computing) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition 020207 software engineering 02 engineering and technology Image (mathematics) Depth map 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Computer vision Point (geometry) Artificial intelligence business ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | CVPR |
| Popis: | We present a deep reinforcement learning method of progressive view inpainting for 3D point scene completion under volume guidance, achieving high-quality scene reconstruction from only a single depth image with severe occlusion. Our approach is end-to-end, consisting of three modules: 3D scene volume reconstruction, 2D depth map inpainting, and multi-view selection for completion. Given a single depth image, our method first goes through the 3D volume branch to obtain a volumetric scene reconstruction as a guide to the next view inpainting step, which attempts to make up the missing information; the third step involves projecting the volume under the same view of the input, concatenating them to complete the current view depth, and integrating all depth into the point cloud. Since the occluded areas are unavailable, we resort to a deep Q-Network to glance around and pick the next best view for large hole completion progressively until a scene is adequately reconstructed while guaranteeing validity. All steps are learned jointly to achieve robust and consistent results. We perform qualitative and quantitative evaluations with extensive experiments on the SUNCG data, obtaining better results than the state of the art. Accepted as CVPR 2019 Oral |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|