Results 1 -
9 of
9
Dominant Orientation Templates for Real-Time Detection of Texture-Less Objects
"... We present a method for real-time 3D object detection that does not require a time consuming training stage, and can handle untextured objects. At its core, is a novel template representation that is designed to be robust to small image transformations. This robustness based on dominant gradient ori ..."
Abstract
-
Cited by 43 (6 self)
- Add to MetaCart
(Show Context)
We present a method for real-time 3D object detection that does not require a time consuming training stage, and can handle untextured objects. At its core, is a novel template representation that is designed to be robust to small image transformations. This robustness based on dominant gradient orientations lets us test only a small subset of all possible pixel locations when parsing the image, and to represent a 3D object with a limited set of templates. We show that together with a binary representation that makes evaluation very fast and a branch-and-bound approach to efficiently scan the image, it can detect untextured objects in complex situations and provide their 3D pose in real-time. 1.
Real-Time Learning of Accurate Patch Rectification
"... Recent work [5, 6] showed that learning-based patch rectification methods are both faster and more reliable than affine region methods. Unfortunately, their performance improvements are founded in a computationally expensive offline learning stage, which is not possible for applications such as SLAM ..."
Abstract
-
Cited by 13 (6 self)
- Add to MetaCart
(Show Context)
Recent work [5, 6] showed that learning-based patch rectification methods are both faster and more reliable than affine region methods. Unfortunately, their performance improvements are founded in a computationally expensive offline learning stage, which is not possible for applications such as SLAM. In this paper we propose an approach whose training stage is fast enough to be performed at run-time without the loss of accuracy or robustness. To this end, we developed a very fast method to compute the mean appearances of the feature points over sets of small variations that span the range of possible camera viewpoints. Then, by simply matching incoming feature points against these mean appearances, we get a coarse estimate of the viewpoint that is refined afterwards. Because there is no need to compute descriptors for the input image, the method is very fast at run-time. We demonstrate our approach on trackingby-detection for SLAM, real-time object detection and pose estimation applications. 1.
Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes
"... Abstract. We propose a framework for automatic modeling, detection, and tracking of 3D objects with a Kinect. The detection part is mainly based on the recent template-based LINEMOD approach [1] for object detection. We show how to build the templates automatically from 3D models, and how to estimat ..."
Abstract
-
Cited by 12 (3 self)
- Add to MetaCart
(Show Context)
Abstract. We propose a framework for automatic modeling, detection, and tracking of 3D objects with a Kinect. The detection part is mainly based on the recent template-based LINEMOD approach [1] for object detection. We show how to build the templates automatically from 3D models, and how to estimate the 6 degrees-of-freedom pose accurately and in real-time. The pose estimation and the color information allow us to check the detection hypotheses and improves the correct detection rate by 13 % with respect to the original LINEMOD. These many improvements make our framework suitable for object manipulation in Robotics applications. Moreover we propose a new dataset made of 15 registered, 1100+ frame video sequences of 15 various objects for the evaluation of future competing methods. Fig. 1. 15 different texture-less 3D objects are simultaneously detected with our approach under different poses on heavy cluttered background with partial occlusion. Each detected object is augmented with its 3D model. We also show the corresponding coordinate systems. 1
Local shape estimation from a single keypoint
- In Proc. Comp. Vis. Patt. Rec. Workshops
, 2010
"... This paper presents a novel approach to estimate local homography of points belong to a given surface. While oth-ers works attempt this by using iterative algorithms devel-oped for template matching, our method introduces a direct estimation of the transformation. It performs the follow-ing steps. F ..."
Abstract
-
Cited by 6 (0 self)
- Add to MetaCart
This paper presents a novel approach to estimate local homography of points belong to a given surface. While oth-ers works attempt this by using iterative algorithms devel-oped for template matching, our method introduces a direct estimation of the transformation. It performs the follow-ing steps. First, a training set of features captures appear-ance and geometry information about keypoints taken from multiple views of the surface. Then incoming keypoints are matched against the training set in order to retrieve a clus-ter of features representing their identity. Finally the re-trieved clusters are used to estimate the local pose of the regions around keypoints. Thanks to the high accuracy, outliers and bad estimates are filtered out by multiscale Summed Square Difference (SSD) test. 1.
Latent-Class Hough Forests for 3D Object Detection and Pose Estimation
"... Abstract. In this paper we propose a novel framework, Latent-Class Hough Forests, for 3D object detection and pose estimation in heavily cluttered and oc-cluded scenes. Firstly, we adapt the state-of-the-art template matching feature, LINEMOD [14], into a scale-invariant patch descriptor and integra ..."
Abstract
-
Cited by 4 (0 self)
- Add to MetaCart
(Show Context)
Abstract. In this paper we propose a novel framework, Latent-Class Hough Forests, for 3D object detection and pose estimation in heavily cluttered and oc-cluded scenes. Firstly, we adapt the state-of-the-art template matching feature, LINEMOD [14], into a scale-invariant patch descriptor and integrate it into a regression forest using a novel template-based split function. In training, rather than explicitly collecting representative negative samples, our method is trained on positive samples only and we treat the class distributions at the leaf nodes as latent variables. During the inference process we iteratively update these dis-tributions, providing accurate estimation of background clutter and foreground occlusions and thus a better detection rate. Furthermore, as a by-product, the la-tent class distributions can provide accurate occlusion aware segmentation masks, even in the multi-instance scenario. In addition to an existing public dataset, which contains only single-instance sequences with large amounts of clutter, we have collected a new, more challenging, dataset for multiple-instance detection containing heavy 2D and 3D clutter as well as foreground occlusions. We evalu-ate the Latent-Class Hough Forest on both of these datasets where we outperform state-of-the art methods. 1
JOINT UTILIZATION OF LOCAL APPEARANCE DESCRIPTORS AND SEMI-LOCAL GEOMETRY FOR MULTI-VIEW OBJECT RECOGNITION
, 2012
"... ..."
Model Guided Multimodal Imaging and Visualization for Computer Assisted Interventions
"... In this short paper, we 1 try to present a summary of some of our activities relevant to computer assisted interventions and computer vision. References to published work in major journals and conferences allow the reader to get access to more detailed information on each subject. It was not possibl ..."
Abstract
- Add to MetaCart
(Show Context)
In this short paper, we 1 try to present a summary of some of our activities relevant to computer assisted interventions and computer vision. References to published work in major journals and conferences allow the reader to get access to more detailed information on each subject. It was not possible to cover all aspects of our research within this paper, but we hope to provide an overview on some of these within this short paper. The readers are also invited to visit our web-site at
Production Services: Newgen Publishing and Data Services
, 2008
"... O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (safari.oreilly.com). For more information, contact our ..."
Abstract
- Add to MetaCart
O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (safari.oreilly.com). For more information, contact our
PAGANI, STRICKER: LEARNING LOCAL PATCH ORIENTATION 1 Learning Local Patch Orientation with a Cascade of Sparse Regressors
"... We present a new method for infering the local 3D orientation of keypoints from their appearance. The method is based on the idea that the relation between keypoint appearance and pose can be learnt efficiently with an adequate regressor. Using one reference view of a keypoint, it is possible to tra ..."
Abstract
- Add to MetaCart
(Show Context)
We present a new method for infering the local 3D orientation of keypoints from their appearance. The method is based on the idea that the relation between keypoint appearance and pose can be learnt efficiently with an adequate regressor. Using one reference view of a keypoint, it is possible to train a keypoint-specific regressor that takes the point appearance as input and delivers the local perspective transformation as output. We show that an elegant choice of regressor is a set of sparse regressors applied sequentially in a cascade. In our case, we use a set of parametrized multivariate relevance vector machines (MVRVM) to learn the local 8-dimensional homography from the patch normalized pixel values. We show that using a cascade of regressors, ranging from coarse pose approximation to fine rectifications, considerably speeds up the identification and pose estimation process. Moreover, we show that our method improves the precision of classical points detectors, as the location of the point is rectified together with the homography. The resulting system is able to recover the orientation of patches in real time. 1
