Open AccessIdentifying 3D Models by Matching Rendered Image and Depth Image with Using a New Combined Descriptor
Author(s) -
Dayou Jiang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1871/1/012062
Subject(s) - artificial intelligence , computer vision , pattern recognition (psychology) , matching (statistics) , feature (linguistics) , histogram , computer science , feature matching , image (mathematics) , identification (biology) , feature detection (computer vision) , feature extraction , invariant (physics) , image processing , mathematics , linguistics , statistics , philosophy , botany , biology , mathematical physics
This paper presented a 3D model identification method by matching pairs of rendered images and depth images. Firstly, the technique uses the HARRIS detector and PIIFD detector (partial intensity invariant feature descriptor) to detect feature points of rendered-depth image pair of shape and then uses LGHD (Log-Gabor Histogram Descriptor) to descript these feature points. Secondly, the shape identification processing is conducted, including normalizing shape pose, capturing multi-view pairs of rendered images and depth images, and image matching. Finally, 100 pairs of 3D models are used for the experiment. The experiment results show that the proposed method has an efficient performance on rendered-depth image matching and can be used for 3D model identification.