3D object recognition from range images using local feature histograms

Hetzel, G.; Leibe, Bastian; Levi, Paul; Schiele, Bernt

doi:10.1109/cvpr.2001.990988

Cited by 155 publications

(95 citation statements)

References 18 publications

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“…3 and 4. The use of contextual or local descriptors is a well-established approach, which has been investigated thoroughly both for appearance-based keypoints in image data (see, e.g., [2,3,52]) and for 3D point clouds (see, e.g., [25,34,74]). …”

Section: Local Contextual Representationmentioning

confidence: 99%

Using surfaces and surface relations in an Early Cognitive Vision system

Kraft

Mustafa

Popovic

et al. 2015

Machine Vision and Applications

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We present a deep hierarchical visual system with two parallel hierarchies for edge and surface information. In the two hierarchies, complementary visual information is represented on different levels of granularity together with the associated uncertainties and confidences. At all levels geometric and appearance information is coded explicitly in 2D and 3D allowing to access this information separately and to link between the different levels. We demonstrate the advantages of such hierarchies in three applications covering grasping, view-point independent object representation, and pose estimation.

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Section: Local Contextual Representationmentioning

confidence: 99%

Using surfaces and surface relations in an Early Cognitive Vision system

Kraft

Mustafa

Popovic

et al. 2015

Machine Vision and Applications

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“…The formal statistical method for assessing the dissimilarity between two probability functions is the χ 2 -test [8]. Another equivalent comparison measure to histograms is the intersection measurement, a similarity function which quantifies the common parts of two histograms.…”

Section: B Pairwise Region Correspondencementioning

confidence: 99%

“…There is a large number of dissimilarity functions to compare histograms, such as euclidean distance, quadratic form distance [3], statistical and probabilistic approaches [13], [8], among others. In this paper we restrict ourselves to statistical similarity functions.…”

Section: B Pairwise Region Correspondencementioning

confidence: 99%

Pairwise region-based scan alignment

Rocha

Druon

Crosnier

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper, we present a new algorithm for the alignment of two 3D scans. The approach uses a region-based matching technique. We make no assumptions about the initial positions of the scans. Regions are described by a probability density function (pdf) computed from low dimensional surface descriptors (curvature or normal cone). The algorithm allows registering directly raw noisy data, possibly with the presence of outliers, without any pre-processing, such as filtering, denoising, or reconstruction. Region correspondence is found using similarity function based on the comparison of regions pdf and under geometry constraints. Results on raw scan data sets are presented to illustrate and evaluate the algorithm.

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“…One of the earliest related approaches, also evaluated on synthetic range images, was proposed in [17], where a histogram representation of a complete point cloud was built merging information of the pixel depths, normal orientations and surface curvatures. More recently, Flint et al [18] defined a descriptor for Hessian-based interest points by accumulating in a histogram the elevation difference of the normals estimated with different sized planes for all points in a support region.…”

Section: D Descriptorsmentioning

confidence: 99%

A 3D descriptor to detect task-oriented grasping points in clothing

Ramisa

Alenyà

Moreno-Noguer

et al. 2016

Pattern Recognition

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Manipulating textile objects with a robot is a challenging task, especially because the garment perception is difficult due to the endless configurations it can adopt, coupled with a large variety of colors and designs. Most current approaches follow a multiple re-grasp strategy, in which clothes are sequentially grasped from different points until one of them yields a recognizable configuration. In this work we propose a method that combines 3D and appearance information to directly select a suitable grasping point for the task at hand, which in our case consists of hanging a shirt or a polo shirt from a hook. Our method follows a coarse-to-fine approach in which, first, the collar of the garment is detected and, next, a grasping point on the lapel is chosen using a novel 3D descriptor.In contrast to current 3D descriptors, ours can run in real time, even when it needs to be densely computed over the input image. Our central idea is to take advantage of the structured nature of range images that most depth sensors provide and, by exploiting integral imaging, achieve speed-ups of two orders of magnitude with respect to competing approaches, while maintaining performance. This makes it especially adequate for robotic applications as we thoroughly demonstrate in the experimental section.

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3D object recognition from range images using local feature histograms

Cited by 155 publications

References 18 publications

Using surfaces and surface relations in an Early Cognitive Vision system

Using surfaces and surface relations in an Early Cognitive Vision system

Pairwise region-based scan alignment

A 3D descriptor to detect task-oriented grasping points in clothing

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