Poster
MarrNet: 3D Shape Reconstruction via 2.5D Sketches
Jiajun Wu · Yifan Wang · Tianfan Xue · Xingyuan Sun · Bill Freeman · Josh Tenenbaum

Tue Dec 5th 06:30 -- 10:30 PM @ Pacific Ballroom #88 #None

3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible 3D shapes. This introduces challenge for learning-based approaches, as 3D object annotations in real images are scarce. Previous work chose to train on synthetic data with ground truth 3D information, but suffered from the domain adaptation issue when tested on real data. In this work, we propose an end-to-end trainable framework, sequentially estimating 2.5D sketches and 3D object shapes. Our disentangled, two-step formulation has three advantages. First, compared to full 3D shape, 2.5D sketches are much easier to be recovered from a 2D image, and to transfer from synthetic to real data. Second, for 3D reconstruction from the 2.5D sketches, we can easily transfer the learned model on synthetic data to real images, as rendered 2.5D sketches are invariant to object appearance variations in real images, including lighting, texture, etc. This further relieves the domain adaptation problem. Third, we derive differentiable projective functions from 3D shape to 2.5D sketches, making the framework end-to-end trainable on real images, requiring no real-image annotations. Our framework achieves state-of-the-art performance on 3D shape reconstruction.

Author Information

Jiajun Wu (MIT)

Jiajun Wu is a fifth-year Ph.D. student at Massachusetts Institute of Technology, advised by Professor Bill Freeman and Professor Josh Tenenbaum. His research interests lie on the intersection of computer vision, machine learning, and computational cognitive science. Before coming to MIT, he received his B.Eng. from Tsinghua University, China, advised by Professor Zhuowen Tu. He has also spent time working at research labs of Microsoft, Facebook, and Baidu.

Yifan Wang (ShanghaiTech University)
Tianfan Xue (MIT CSAIL)

Tianfan Xue is currently a fifth-year Ph.D. student in MIT CSAIL. Before that, he received his B.E. degree from Tsinghua Universtiy, and M.Phil. degree from The Chinese University of Hong Kong. His research interests include computer vision, image processing, and machine learning.

Xingyuan Sun (Shanghai Jiao Tong University)
Bill Freeman (MIT/Google)
Josh Tenenbaum (MIT)

Josh Tenenbaum is an Associate Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory (CSAIL). He received his PhD from MIT in 1999, and was an Assistant Professor at Stanford University from 1999 to 2002. He studies learning and inference in humans and machines, with the twin goals of understanding human intelligence in computational terms and bringing computers closer to human capacities. He focuses on problems of inductive generalization from limited data -- learning concepts and word meanings, inferring causal relations or goals -- and learning abstract knowledge that supports these inductive leaps in the form of probabilistic generative models or 'intuitive theories'. He has also developed several novel machine learning methods inspired by human learning and perception, most notably Isomap, an approach to unsupervised learning of nonlinear manifolds in high-dimensional data. He has been Associate Editor for the journal Cognitive Science, has been active on program committees for the CogSci and NIPS conferences, and has co-organized a number of workshops, tutorials and summer schools in human and machine learning. Several of his papers have received outstanding paper awards or best student paper awards at the IEEE Computer Vision and Pattern Recognition (CVPR), NIPS, and Cognitive Science conferences. He is the recipient of the New Investigator Award from the Society for Mathematical Psychology (2005), the Early Investigator Award from the Society of Experimental Psychologists (2007), and the Distinguished Scientific Award for Early Career Contribution to Psychology (in the area of cognition and human learning) from the American Psychological Association (2008).

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