Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
Name
1804.04610.pdf
Description
Accepted version
Size
9.3 MB
Format
Unknown
Checksum (MD5)
7bb1a9e94d6bba3f215b4c4720b7e117
Author(s)
Sun, Xingyuan
Wu, Jiajun
Zhang, Xiuming
Zhang, Zhoutong
Zhang, Chengkai
Xue, Tianfan
Tenenbaum, Joshua B
Freeman, William T
Date Issued
2018
Version
Author's final manuscript
Abstract
© 2018 IEEE. We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications in shape-related tasks including reconstruction, retrieval, viewpoint estimation, etc. Building such a large-scale dataset, however, is highly challenging; existing datasets either contain only synthetic data, or lack precise alignment between 2D images and 3D shapes, or only have a small number of images. Second, we calibrate the evaluation criteria for 3D shape reconstruction through behavioral studies, and use them to objectively and systematically benchmark cutting-edge reconstruction algorithms on Pix3D. Third, we design a novel model that simultaneously performs 3D reconstruction and pose estimation; our multi-task learning approach achieves state-of-the-art performance on both tasks.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPR.2018.00314
