Advanced Planar Projection Contour (PPC): A Novel Algorithm for Local Feature Description in Point Clouds

Local feature description of point clouds is essential in 3D computer vision. However, many local feature descriptors for point clouds struggle with inadequate robustness, excessive dimensionality, and poor computational efficiency. To address these issues, we propose a novel descriptor based on Pla...

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Bibliographic Details
Published in:Journal of imaging 2024-04, Vol.10 (4), p.84
Main Authors: Tang, Wenbin, Lv, Yinghao, Chen, Yongdang, Zheng, Linqing, Wang, Runxiao
Format: Article
Language:English
Subjects:
Algorithms
Comparative analysis
Computer vision
Construction
Contours
convex hull contour
Convexity
Cultural heritage
Design and construction
Histograms
local feature descriptor
local reference frame
Machine vision
Orthogonality
Robustness (mathematics)
Three dimensional models
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Summary:Local feature description of point clouds is essential in 3D computer vision. However, many local feature descriptors for point clouds struggle with inadequate robustness, excessive dimensionality, and poor computational efficiency. To address these issues, we propose a novel descriptor based on Planar Projection Contours, characterized by convex packet contour information. We construct the Local Reference Frame (LRF) through covariance analysis of the query point and its neighboring points. Neighboring points are projected onto three orthogonal planes defined by the LRF. These projection points on the planes are fitted into convex hull contours and encoded as local features. These planar features are then concatenated to create the Planar Projection Contour (PPC) descriptor. We evaluated the performance of the PPC descriptor against classical descriptors using the B3R, UWAOR, and Kinect datasets. Experimental results demonstrate that the PPC descriptor achieves an accuracy exceeding 80% across all recall levels, even under high-noise and point density variation conditions, underscoring its effectiveness and robustness.
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging10040084