Relative positioning with uncalibrated cameras
Geometric invariance in computer vision
Segmentation of range images as the search for geometric parametric models
International Journal of Computer Vision
Autocalibration from Planar Scenes
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Multibody Structure and Motion: 3-D Reconstruction of Independently Moving Objects
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Visual Detection of Obstacles Assuming a Locally Planar Ground
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
A multi-body factorization method for motion analysis
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Using Geometric Constraints for Matching Disparate Stereo Views of 3D Scenes Containing Planes
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
An Invitation to 3-D Vision: From Images to Geometric Models
An Invitation to 3-D Vision: From Images to Geometric Models
An Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A random sampling strategy for piecewise planar scene segmentation
Computer Vision and Image Understanding
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
A Model-Selection Framework for Multibody Structure-and-Motion of Image Sequences
International Journal of Computer Vision
Robust Multiple Structures Estimation with J-Linkage
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Improving the Agility of Keyframe-Based SLAM
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
SBA: A software package for generic sparse bundle adjustment
ACM Transactions on Mathematical Software (TOMS)
Structure-from-motion using lines: Representation, triangulation, and bundle adjustment
Computer Vision and Image Understanding
Object recognition and full pose registration from a single image for robotic manipulation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Piecewise planar scene reconstruction from sparse correspondences
Image and Vision Computing
Multibody Structure-from-Motion in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
Metric reconstruction of planes utilizing off-the-plane features
Computer Vision and Image Understanding
Accelerated hypothesis generation for multi-structure robust fitting
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Multiple Plane Detection in Image Pairs Using J-Linkage
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
CC-RANSAC: Fitting planes in the presence of multiple surfaces in range data
Pattern Recognition Letters
3D Scene interpretation by combining probability theory and logic: The tower of knowledge
Computer Vision and Image Understanding
| Hi-index | 0.00 |
Detecting elements such as planes in 3D is essential to describe objects for applications such as robotics and augmented reality. While plane estimation is well studied, table-top scenes exhibit a large number of planes and methods often lock onto a dominant plane or do not estimate 3D object structure but only homographies of individual planes. In this paper we introduce MDL to the problem of incrementally detecting multiple planar patches in a scene using tracked interest points in image sequences. Planar patches are reconstructed and stored in a keyframe-based graph structure. In case different motions occur, separate object hypotheses are modelled from currently visible patches and patches seen in previous frames. We evaluate our approach on a standard data set published by the Visual Geometry Group at the University of Oxford [24] and on our own data set containing table-top scenes. Results indicate that our approach significantly improves over the state-of-the-art algorithms.