Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

• Published in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 2974 - 2983
• Main Authors: Sun, Xingyuan, Wu, Jiajun, Zhang, Xiuming, Zhang, Zhoutong, Zhang, Chengkai, Xue, Tianfan, Tenenbaum, Joshua B., Freeman, William T.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2018
• Subjects: Benchmark testing; Image reconstruction; Pose estimation; Shape; Solid modeling; Three-dimensional displays; Two dimensional displays
• ISSN: 1063-6919
• DOI: 10.1109/CVPR.2018.00314

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.