@inproceedings{0d02250ce9194f2db7aeecfe01cfab5b,
title = "Point Pair Feature Matching: Evaluating Methods to Detect Simple Shapes",
abstract = "A recent benchmark for 3D object detection and 6D pose estimation from RGB-D images shows the dominance of methods based on Point Pair Feature Matching (PPFM). Since its invention in 2010 several modifications have been proposed to cope with its weaknesses, which are computational complexity, sensitivity to noise, and difficulties in the detection of geometrically simple objects with planar surfaces and rotational symmetries. In this work we focus on the latter. We present a novel approach to automatically detect rotational symmetries by matching the object model to itself. Furthermore, we adapt methods for pose verification and use more discriminative features which incorporate global information into the Point Pair Feature. We also examine the effects of other, already existing extensions by testing them on our specialized dataset for geometrically primitive objects. Results show that particularly our handling of symmetries and the augmented features are able to boost recognition rates.",
keywords = "Object detection, Point Pair Features, Pose estimation",
author = "Markus Ziegler and Martin Rudorfer and Xaver Kroischke and Sebastian Krone and J{\"o}rg Kr{\"u}ger",
year = "2019",
doi = "10.1007/978-3-030-34995-0_40",
language = "English",
isbn = "9783030349943",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Vieweg",
pages = "445--456",
editor = "Dimitrios Tzovaras and Dimitrios Giakoumis and Markus Vincze and Antonis Argyros",
booktitle = "Computer Vision Systems - 12th International Conference, ICVS 2019, Proceedings",
address = "Germany",
note = "12th International Conference on Computer Vision Systems, ICVS 2019 ; Conference date: 23-09-2019 Through 25-09-2019",
}