Arboricity and subgraph listing algorithms
SIAM Journal on Computing
On generating all maximal independent sets
Information Processing Letters
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Geometric Information Criterion for Model Selection
International Journal of Computer Vision
A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Recognizing Objects Using Color-Annotated Adjacency Graphs
Shape, Contour and Grouping in Computer Vision
Real-Time Detection of Independent Motion using Stereo
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Generalized Principal Component Analysis (GPCA)
IEEE Transactions on Pattern Analysis and Machine Intelligence
The worst-case time complexity for generating all maximal cliques and computational experiments
Theoretical Computer Science - Computing and combinatorics
Listing all maximal cliques in large sparse real-world graphs
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
Perspective n-view multibody structure-and-motion through model selection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A Simple and Practical Solution to the Rigid Body Motion Segmentation Problem Using a RGB-D Camera
DICTA '11 Proceedings of the 2011 International Conference on Digital Image Computing: Techniques and Applications
Fast Approximate Energy Minimization with Label Costs
International Journal of Computer Vision
Image segmentation by figure-ground composition into maximal cliques
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Motion segmentation is a key underlying problem in computer vision for dynamic scenes. Given 3D data from a RGB-D camera, this paper presents a novel method for motion segmentation without explicitly estimating motions. Building up from a recent literature [1] that proposes a similarity measure between two 3D points belonging to a rigid body, we show that identifying rigid motion groups corresponds to a maximal clique enumeration problem of the similarity graph. Using efficient maximal clique enumeration algorithms we show that it is practically feasible to find all the unique candidate motion groups in a deterministic fashion. We investigate the relationship to traditional hypothesis sampling and show that under certain conditions the inliers to a hypothesis form a clique in the similarity graph. Further, we show that identifying true motions from the candidate motions can be cast as a minimum set cover problem (for outlier-free data) or a max k-cover problem (for data with outliers). This allows us to use the greedy algorithm for max k-cover to segment the motion groups. Presented results using synthetic and real RGB-D data show the validity of our approach.