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

, , , , , , , and . (2018)cite arxiv:1804.04610Comment: CVPR 2018. The first two authors contributed equally to this work. Project page: http://pix3d.csail.mit.edu.


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.


[1804.04610] Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

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