Fast Point Feature Histograms (FPFH) for 3D registration

Rusu, Radu Bogdan; Blodow, Nico; Beetz, Michael

doi:10.1109/robot.2009.5152473

Cited by 3,015 publications

(1,917 citation statements)

References 15 publications

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“…Since then, several approaches to object recognition using 3D data have been proposed. Rusu et al developed a global descriptor named VFH [39], following its local predecessor FPFH [40]. This idea was soon extended in [2] to form a Clustered Viewpoint Feature Histogram, which is more robust to occlusions.…”

Section: Related Workmentioning

confidence: 99%

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3D Object Detection and Recognition for Robotic Grasping Based on RGB-D Images and Global Features

Czajewski

Kołomyjec

2017

Foundations of Computing and Decision Sciences

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Abstract. This paper describes the results of experiments on detection and recognition of 3D objects in RGB-D images provided by the Microsoft Kinect sensor. While the studies focus on single image use, sequences of frames are also considered and evaluated. Observed objects are categorized based on both geometrical and visual cues, but the emphasis is laid on the performance of the point cloud matching method. To this end, a rarely used approach consisting of independent VFH and CRH descriptors matching, followed by ICP and HV algorithms from the Point Cloud Library is applied. Successfully recognized objects are then subjected to a classical 2D analysis based on color histogram comparison exclusively with objects in the same geometrical category. The proposed two-stage approach allows to distinguish objects of similar geometry and different visual appearance, like soda cans of various brands. By separating geometry and color identification phases, the applied system is still able to categorize objects based on their geometry, even if there is no color match. The recognized objects are then localized in the three-dimensional space and autonomously grasped by a manipulator. To evaluate this approach, a special validation set was created, and additionally a selected scene from the Washington RGB-D Object Dataset was used.

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Section: Related Workmentioning

confidence: 99%

“…The Viewpoint Feature Histogram (VFH) [2], [39] is based on the local Fast Point Feature Histograms (FPFH) [40]. It consists of two parts: the viewing direction component and the extended FPFH component.…”

Section: Viewpoint Feature Histogram (Vfh) Descriptormentioning

confidence: 99%

3D Object Detection and Recognition for Robotic Grasping Based on RGB-D Images and Global Features

Czajewski

Kołomyjec

2017

Foundations of Computing and Decision Sciences

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“…Rusu etal. [34] propose Point Feature Histograms describing the local geometry of a point and its k nearest neighbours. Knopp etal.…”

Section: Global Approaches Vs Local Approachesmentioning

confidence: 99%

Scale-Invariant Vote-Based 3D Recognition and Registration from Point Clouds

Pham

Woodford

Perbet

et al. 2013

Machine Learning for Computer Vision

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Abstract. This chapter presents a method for vote-based 3D shape recognition and registration, in particular using mean shift on 3D pose votes in the space of direct similarity transformations for the first time. We introduce a new distance between poses in this space-the SRT distance. It is left-invariant, unlike Euclidean distance, and has a unique, closed-form mean, in contrast to Riemannian distance, so is fast to compute. We demonstrate improved performance over the state of the art in both recognition and registration on a (real and) challenging dataset, by comparing our distance with others in a mean shift framework, as well as with the commonly used Hough voting approach.

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“…This issue is mentioned even by the authors of the most recent segmentation-based object recognition systems, such as [15]. As an alternative, in recent years multiple techniques of neighborhood-based, local 3D surface description have been developed, such as PFH [23], FPFH [21], PFHRGB [4], CVFH [2]. The applications for such descriptors range from point cloud registration to object part recognition, fast orientation retrieval and, as descriptors become more accurate, raise the possibility of segmentation-less object detection.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…PFH, FPFH, PFHRGB [23,21,4]. The former are multi-dimensional histograms of parameters describing the relations of point pairs position and normal vectors, whereas FPFH is an approximate speed-up version of PFH and PFHRGB is PFH with three added histograms representing the RGB color relations between these point pairs.…”

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confidence: 99%

Keypoint-Less, Heuristic Application of Local 3D Descriptors

Harasymowicz-Boggio

Chechliński

2017

Foundations of Computing and Decision Sciences

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Abstract. One of the most important topics in the research concerning 3D local descriptors is computational efficiency. The state-of-the-art approach addressing this matter consists in using keypoint detectors that effectively limit the number of points for which the descriptors are computed. However, the choice of keypoints is not trivial and might have negative implications, such as the omission of relevant areas. Instead, focusing on the task of single object detection, we propose a keypoint-less approach to attention focusing in which the full scene is processed in a hierarchical manner: weaker, less rejective and faster classification methods are used as heuristics for increasingly robust descriptors, which allows to use more demanding algorithms at the top level of the hierarchy. We have developed a massively-parallel, open source object recognition framework, which we use to explore the proposed method on demanding, realistic indoor scenes, applying the full power available in modern computers.

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Fast Point Feature Histograms (FPFH) for 3D registration

Cited by 3,015 publications

References 15 publications

3D Object Detection and Recognition for Robotic Grasping Based on RGB-D Images and Global Features

3D Object Detection and Recognition for Robotic Grasping Based on RGB-D Images and Global Features

Scale-Invariant Vote-Based 3D Recognition and Registration from Point Clouds

Keypoint-Less, Heuristic Application of Local 3D Descriptors

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