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Roberto Tron
Researcher at Boston University
Publications - 100
Citations - 3646
Roberto Tron is an academic researcher from Boston University. The author has contributed to research in topics: Computer science & Distributed algorithm. The author has an hindex of 23, co-authored 82 publications receiving 3106 citations. Previous affiliations of Roberto Tron include Johns Hopkins University & University of Pennsylvania.
Papers
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Proceedings ArticleDOI
A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms
Roberto Tron,René Vidal +1 more
TL;DR: This paper compares four 3D motion segmentation algorithms for affine cameras on a benchmark of 155 motion sequences of checkerboard, traffic, and articulated scenes.
Journal ArticleDOI
Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted Trajectories
TL;DR: A robust subspace separation scheme is developed that deals with practical issues in a unified mathematical framework and gives surprisingly good performance in the presence of the three types of pathological trajectories mentioned above.
Proceedings ArticleDOI
Motion segmentation via robust subspace separation in the presence of outlying, incomplete, or corrupted trajectories
TL;DR: A robust subspace separation scheme that can deal with all of these practical issues in a unified framework and draw strong connections between lossy compression, rank minimization, and sparse representation is developed.
Journal ArticleDOI
Multiframe Motion Segmentation with Missing Data Using PowerFactorization and GPCA
TL;DR: This algorithm involves projecting all point trajectories onto a 5-dimensional subspace using the SVD, the PowerFactorization method, or RANSAC, and fitting multiple linear subspaces representing different rigid-body motions to the points in ℝ5 using GPCA.
Proceedings ArticleDOI
Initialization techniques for 3D SLAM: A survey on rotation estimation and its use in pose graph optimization
TL;DR: It is shown that the use of rotation estimation to bootstrap iterative pose graph solvers entails significant boost in convergence speed and robustness.