21 Station Road
Cambridge
CB1 2FB
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Frontiers in Artificial Intelligence is a series of public lectures at Microsoft Research Cambridge featuring leading researchers in the field, focusing on the cutting edge topics at the intersection of machine learning, statistics, and artificial intelligence. Students, scientists, and engineers in academia and industry are all welcome to join us for these exciting talks and the opportunity to socialize with the Cambridge AI/ML community.
Probabilistic and Deep Models for 3D Reconstruction
Andreas Geiger – Max Planck Institute for Intelligent Systems
3D reconstruction from multiple 2D images is an inherently ill-posed problem. Prior knowledge is required to resolve ambiguities and probabilistic models are desirable to capture the ambiguities in the reconstructed model. In this talk, I will present two recent results tackling these two aspects. First, I will introduce a probabilistic framework for volumetric 3D reconstruction where the reconstruction problem is cast as inference in a Markov random field using ray potentials. Our main contribution is a discrete-continuous inference algorithm which computes marginal distributions of each voxel’s occupancy and appearance. I will show that the proposed algorithm allows for Bayes optimal predictions with respect to a natural reconstruction loss. I will further demonstrate several extensions which integrate non-local CAD priors into the reconstruction process. In the second part of my talk, I will present a novel framework for deep learning with 3D data called OctNet which enables 3D CNNs on high-dimensional inputs. I will demonstrate the utility of the OctNet representation on several 3D tasks including classification, orientation estimation and point cloud labeling. Finally, I will present an extension of OctNet called OctNetFusion which jointly predicts the space partitioning function with the output representation, resulting in an end-to-end trainable model for volumetric depth map fusion.