When 3D Reconstruction Meets Ubiquitous RGB-D Images

Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 700-707

Abstract


3D reconstruction from a single image is a classical problem in computer vision. However, it still poses great challenges for the reconstruction of daily-use objects with irregular shapes. In this paper, we propose to learn 3D reconstruction knowledge from informally captured RGB-D images, which will probably be ubiquitously used in daily life. The learning of 3D reconstruction is defined as a category modeling problem, in which a model for each category is trained to encode category-specific knowledge for 3D reconstruction. The category model estimates the pixel-level 3D structure of an object from its 2D appearance, by taking into account considerable variations in rotation, 3D structure, and texture. Learning 3D reconstruction from ubiquitous RGB-D images creates a new set of challenges. Experimental results have demonstrated the effectiveness of the proposed approach.

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[bibtex]
@InProceedings{Zhang_2014_CVPR,
author = {Zhang, Quanshi and Song, Xuan and Shao, Xiaowei and Zhao, Huijing and Shibasaki, Ryosuke},
title = {When 3D Reconstruction Meets Ubiquitous RGB-D Images},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}