Keypoint-less, heuristic application of local 3D descriptors

• Autorzy: Harasymowicz-Boggio B. , Chechliński Ł.

Identyfikatory

DOI

10.1515/fcds-2017-0012

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

RGB-D; object detection; keypoint -less; 3D description

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2017
• Tom: Vol. 42, No. 3
• Strony: 240--255
• Opis fizyczny: Bibliogr. 27 poz., fig., tab.

Twórcy

autor

Harasymowicz-Boggio B.

• Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland

autor

Chechliński Ł.

• Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland

Bibliografia

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).