Unique Signatures of Histograms for Local Surface Description

Tombari, Federico; Salti, Samuele; Stefano, Luigi Di

doi:10.1007/978-3-642-15558-1_26

Cited by 1,084 publications

(1,097 citation statements)

References 23 publications

Supporting

Mentioning

1,084

Contrasting

Unclassified

Order By: Relevance

“…A large portion of existing literature in instance recognition is dedicated to building descriptors to improve recognition [23,2]. In this work we use simple edge/normal orientation features and focus on achieving robust recognition in presence of clutter, similar looking distractors and occlusion.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Instance Recognition in Presence of Occlusion and Clutter

Bonde

Badrinarayanan

Cipolla

2014

Computer Vision – ECCV 2014

View full text Add to dashboard Cite

Abstract. We present a robust learning based instance recognition framework from single view point clouds. Our framework is able to handle real-world instance recognition challenges, i.e, clutter, similar looking distractors and occlusion. Recent algorithms have separately tried to address the problem of clutter [9] and occlusion [16] but fail when these challenges are combined. In comparison we handle all challenges within a single framework. Our framework uses a soft label Random Forest [5] to learn discriminative shape features of an object and use them to classify both its location and pose. We propose a novel iterative training scheme for forests which maximizes the margin between classes to improve recognition accuracy, as compared to a conventional training procedure. The learnt forest outperforms template matching, DPM [7] in presence of similar looking distractors. Using occlusion information, computed from the depth data, the forest learns to emphasize the shape features from the visible regions thus making it robust to occlusion. We benchmark our system with the state-of-the-art recognition systems [9,7] in challenging scenes drawn from the largest publicly available dataset. To complement the lack of occlusion tests in this dataset, we introduce our Desk3D dataset and demonstrate that our algorithm outperforms other methods in all settings.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Robust Instance Recognition in Presence of Occlusion and Clutter

Bonde

Badrinarayanan

Cipolla

2014

Computer Vision – ECCV 2014

View full text Add to dashboard Cite

show abstract

“…Further research on the features of the 3D objects resulted in the appearance of OUR-CVFH descriptor based on Global Unique Reference Frames [5] computed for each cluster. The work described in [44] shows the application results of the Signature of Histograms of OrienTations (SHOT) -a local descriptor inspired by SIRF, which represents an intersection between Histograms and Signatures with high computational efficiency. In [3], a correspondence grouping algorithm and a fast descriptor matching accomplished by the FLANN Library [32] was proposed.…”

Section: Related Workmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…This descriptor is based on the SHOT descriptor [6], that obtains a repeatable local reference frame using the eigenvalue decomposition around an input point. Given this reference frame, a spherical grid centered on the point divides the neighborhood so that in each grid bin a weighted histogram of normals is obtained.…”

Section: Descriptorsmentioning

confidence: 99%

Set Distance Functions for 3D Object Recognition

Alexandre

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

Abstract. One of the key steps in 3D object recognition is the matching between an input cloud and a cloud in a database of known objects. This is usually done using a distance function between sets of descriptors. In this paper we propose to study how several distance functions (some already available and other new proposals) behave experimentally using a large freely available household object database containing 1421 point clouds from 48 objects and 10 categories. We present experiments illustrating the accuracy of the distances both for object and category recognition and find that simple distances give competitive results both in terms of accuracy and speed.

show abstract

Unique Signatures of Histograms for Local Surface Description

Cited by 1,084 publications

References 23 publications

Robust Instance Recognition in Presence of Occlusion and Clutter

Robust Instance Recognition in Presence of Occlusion and Clutter

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

Set Distance Functions for 3D Object Recognition

Contact Info

Product

Resources

About