CLC number: TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
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Yuan HUANG, Feipeng DA. Three-dimensional face point cloud hole-filling algorithm based on binocular stereo matching and a B-spline[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(3): 398-408.
@article{title="Three-dimensional face point cloud hole-filling algorithm based on binocular stereo matching and a B-spline",
author="Yuan HUANG, Feipeng DA",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="3",
pages="398-408",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000508"
}
%0 Journal Article
%T Three-dimensional face point cloud hole-filling algorithm based on binocular stereo matching and a B-spline
%A Yuan HUANG
%A Feipeng DA
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 3
%P 398-408
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000508
TY - JOUR
T1 - Three-dimensional face point cloud hole-filling algorithm based on binocular stereo matching and a B-spline
A1 - Yuan HUANG
A1 - Feipeng DA
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 3
SP - 398
EP - 408
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000508
Abstract: When obtaining three-dimensional (3D) face point cloud data based on structured light, factors related to the environment, occlusion, and illumination intensity lead to holes in the collected data, which affect subsequent recognition. In this study, we propose a hole-filling method based on stereo-matching technology combined with a b-spline. The algorithm uses phase information acquired during raster projection to locate holes in the point cloud, simultaneously extracting boundary point cloud sets. By registering the face point cloud data using the stereo-matching algorithm and the data collected using the raster projection method, some supplementary information points can be obtained at the holes. The shape of the b-spline curve can then be roughly described by a few key points, and the control points are put into the hole area as key points for iterative calculation of surface reconstruction. Simulations using smooth ceramic cups and human face models showed that our model can accurately reproduce details and accurately restore complex shapes on the test surfaces. Simulation results indicated the robustness of the method, which is able to fill holes on complex areas such as the inner side of the nose without a prior model. This approach also effectively supplements the hole information, and the patched point cloud is closer to the original data. This method could be used across a wide range of applications requiring accurate facial recognition.
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