15 citations · 22 across the 4 of their papers we have counts for
6 papers
Seeing What Is Not There: Learning Context to Determine Where Objects Are Missing
Jin Sun, David W. Jacobs
Most of computer vision focuses on what is in an image. We propose to train a standalone object-centric context representation to perform the opposite task: seeing what is not ther…
A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery
Soumyadip Sengupta, Tal Amir, Meirav Galun +4
Accurate estimation of camera matrices is an important step in structure from motion algorithms. In this paper we introduce a novel rank constraint on collections of fundamental ma…
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability
Soumyadip Sengupta, Hao Zhou, Walter Forkel +3
We introduce a new, integrated approach to uncalibrated photometric stereo. We perform 3D reconstruction of Lambertian objects using multiple images produced by unknown, directiona…
WarpNet: Weakly Supervised Matching for Single-view Reconstruction
Angjoo Kanazawa, David W. Jacobs, Manmohan Chandraker
We present an approach to matching images of objects in fine-grained datasets without using part annotations, with an application to the challenging problem of weakly supervised si…
Efficient Representation of Low-Dimensional Manifolds using Deep Networks
Ronen Basri, David Jacobs
We consider the ability of deep neural networks to represent data that lies near a low-dimensional manifold in a high-dimensional space. We show that deep networks can efficiently…
Deep Hierarchical Parsing for Semantic Segmentation
Abhishek Sharma, Oncel Tuzel, David W. Jacobs
This paper proposes a learning-based approach to scene parsing inspired by the deep Recursive Context Propagation Network (RCPN). RCPN is a deep feed-forward neural network that ut…