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Daniel Cremers
Researcher at Technische Universität München
Publications - 702
Citations - 55592
Daniel Cremers is an academic researcher from Technische Universität München. The author has contributed to research in topics: Image segmentation & Computer science. The author has an hindex of 99, co-authored 655 publications receiving 44957 citations. Previous affiliations of Daniel Cremers include Siemens & University of Mannheim.
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A Variational Approach to Shape-from-shading Under Natural Illumination
TL;DR: An augmented Lagrangian approach for solving a generic PDE-based shape-from-shading model which handles directional or spherical harmonic lighting, orthographic or perspective projection, and greylevel or multi-channel images is presented.
Patent
Statistical priors for combinatorial optimization: efficient solutions via graph cuts
Daniel Cremers,Leo Grady +1 more
TL;DR: In this article, a framework to learn and impose prior knowledge on the distribution of pairs and triplets of labels via graph cuts is presented, which optimally restore binary textures from very noisy images with runtimes in the order of seconds while imposing hundreds of statistically learned constraints per node.
Posted Content
DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization.
Juan Du,Rui Wang,Daniel Cremers +2 more
TL;DR: In this paper, a Siamese network is proposed to jointly learn 3D local feature detection and description directly from raw 3D points, which achieves competitive results for both global point cloud retrieval and local point cloud registration in comparison to state-of-theart approaches.
Journal ArticleDOI
The homotopy method revisited: Computing solution paths of ℓ₁-regularized problems
TL;DR: A generalized homotopy algorithm based on a nonnegative least squares problem, which does not require such a condition, and is the first to provably compute a full solution path for an arbitrary combination of an input matrix and a data vector.
Book ChapterDOI
MRF Optimization with Separable Convex Prior on Partially Ordered Labels
TL;DR: This paper introduces a generalization to partially ordered sets and proposes an efficient coarse-to-fine approach over the label space that provides an approximate solution to the multi-labeling problem with a convex penalty.