期刊
IEEE SENSORS JOURNAL
卷 20, 期 20, 页码 12049-12056出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2020.2968477
关键词
Three-dimensional displays; Cameras; Solid modeling; Sensors; Computational modeling; Geometry; Real-time systems; 3D Reconstruction; structure from motion; depth map; camera calibration; multi-view geometry
资金
- National Key Research and Development Plan [2016YFC0800100]
- National Natural Science Foundation [61972128, 61802103, 61877016, 61602146, 61673157]
- China Postdoctoral Science Foundation [2018M632522]
- Fundamental Research Funds for the Central Universities [PA2019GDPK0071, JZ2018HGBH0280, PA2018GDQT0014]
- Key Research and Development Program in Anhui Province [1804a09020036]
Image-based 3D reconstruction is a fundamental task in computer vision and computer graphics. Although many existing approaches have obtained excellent results, they all dependent on multi-view images, then these methods are very complex. To reduce the complexity of the existing approaches, in this paper we fist propose a novel 3D reconstruction method based on single RGB image and the corresponding depth image, and design a unified framework called Single3D. Second, we trained a neural network model to reduce noises for improving the quality of the input RGB image, then leading a desirable RGB image. Third, we combine the deep guided filter and fast guided filter to fill holes on the depth image for improving the quality of 3D model. Fourth, we recover high-quality 3D model from the RGB-D image based on the proposed Single3D framework. Finally, the comprehensive experiments were conducted on the most challenging benchmarking dataset with variant light, occlusion, and clustering. Experimental results show that our method has desirable accuracy and efficiency.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据