3ddl.cs.princeton.edu

-- You are using an outdated browser. Please upgrade your browser to improve your experience. 3DDL at NIPS 2016 HOME Overview Speakers Schedule Team Submission 3D Deep Learning Workshop @ NIPS 2016 Overview Deep learning is proven to be a powerful tool to build models for language (one-dimensional) and image (two-dimensional) understanding. Tremendous efforts have been devoted to these areas, however, it is still at the early stage to apply deep learning to 3D data, despite their great research values and broad real-world applications. In particular, existing methods poorly serve the three-dimensional data that drives a broad range of critical applications such as augmented reality, autonomous driving, graphics, robotics, medical imaging, neuroscience, and scientific simulations. These problems have drawn the attention of researchers in different fields such as neuroscience, computer vision, and graphics. The goal of this workshop is to foster interdisciplinary communication of researchers working on 3D data (Computer Vision and Computer Graphics) so that more attention of broader community can be drawn to 3D deep learning problems. Through those studies, new ideas and discoveries are expected to emerge, which can inspire advances in related fields. This workshop is composed of invited talks, oral presentations of outstanding submissions and a poster session to showcase the state-of-the-art results on the topic. In particular, a panel discussion among leading researchers in the field is planned, so as to provide a common playground for inspiring discussions and stimulating debates. The workshop will be held on Dec 9 at NIPS 2016 in Barcelona, Spain . Speakers Thomas Brox Thomas Funkhouser -- -- -- -- Dinesh Manocha -- -- -- -- Abhinav Gupta Michael Bronstein Hao Su Schedule Dec 9, 2016 at Room 115 Time Speaker Slides Topic 08:30 - 08:45 Fisher Yu Welcome 08:45 - 09:15 Thomas Brox PDF Learning 3D representations, disparity estimation, and structure from motion 09:15 - 09:45 Thomas Funkhouser PDF Scene Understanding with 3D Deep Networks 09:45 - 10:30 Coffee Break 10:30 - 11:00 Hao Su PDF 3D object reconstruction and abstraction by deep learning 11:00 - 11:30 Jiajun Wu PDF Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling 11:30 - 12:00 Reza Zadeh PDF FusionNet: 3D Object Classification Using Multiple Data Representations 12:00 - 14:30 Lunch & Poster session 14:30 - 15:00 Michael Bronstein PDF Learning deformable 3D correspondence with intrinsic convolutional neural networks 15:00 - 15:30 Andrew Brock PPT Generative and Discriminative Voxel Modeling with Convolutional Neural Networks 15:30 - 16:00 Coffee break 16:00 - 16:30 Abhinav Gupta KEY Representing 3D: From Surface Normals to Voxels 16:30 - 17:00 Alexandr Notchenko PDF Sparse 3D Convolutional Neural Networks for Large-Scale Shape Retrieval 17:00 - 17:30 Jianxiong Xiao Panel Discussion 17:30 - 17:45 Closing Abstracts Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman and Joshua Tenenbaum Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling Abstract - Poster - Webpage - Code Charlie Nash and Chris Williams Generative models of part-structured 3D objects Abstract - Poster Taylor Dahlke, Mauricio Araya-Polo, Chiyuan Zhang and Charlie Frogner Predicting geological features in 3D Seismic Data Abstract - Poster Akshay Rangamani, Tao Xiong, Arun A Nair, Trac Tran and Sang Chin Landmark Detection and Tracking in Ultrasound using a CNN-RNN Framework Organizers Fisher Yu - Princeton University Joseph Lim - Stanford University Matthew Fisher - Stanford University Qixing Huang - University of Texas at Austin Jianxiong Xiao - AutoX Inc....