Abstract
The reconstruction of 3D face data is widely used in the fields of biometric recognition and virtual reality. However, the rapid acquisition of 3D data is plagued by reconstruction accuracy, slow speed, excessive scenes and contemporary reconstruction-technology. To solve this problem, an accurate 3D face-imaging implementation framework based on coarse-to-fine spatiotemporal correlation is designed, improving the spatiotemporal correlation stereo matching process and accelerating the processing using a spatiotemporal box filter. The reliability of the reconstruction parameters is further verified in order to resolve the contention between the measurement accuracy and time cost. A binocular 3D data acquisition device with a rotary speckle projector is used to continuously and synchronously acquire an infrared speckle stereo image sequence for reconstructing an accurate 3D face model. Based on the face mask data obtained by the high-precision industrial 3D scanner, the relationship between the number of projected speckle patterns, the matching window size, the reconstruction accuracy and the time cost is quantitatively analysed. An optimal combination of parameters is used to achieve a balance between reconstruction speed and accuracy. Thus, to overcome the problem of a long acquisition time caused by the switching of the rotary speckle pattern, a compact 3D face acquisition device using a fixed three-speckle projector is designed. Using the optimal combination parameters of the three speckles, the parallel pipeline strategy is adopted in each core processing unit to maximise system resource utilisation and data throughput. The most time-consuming spatiotemporal correlation stereo matching activity was accelerated by the graphical processing unit. The results show that the system achieves real-time image acquisition, as well as 3D face reconstruction, while maintaining acceptable systematic precision.
Highlights
In the field of computer vision and computer graphics, acquiring, modelling and synthesising three-dimensional (3D) human faces has become an active research topic
A 3D face acquisition device with a rotating speckle projector is more suitable for use in scenes where the accuracy requirements are strict, and there is no clear limitation on the acquisition time
To improve the accuracy and performance of 3D face imaging, an effective coarse-tofine spatiotemporal stereo matching scheme using speckle pattern projection was proposed, which was accelerated by spatiotemporal box filter (STBF)
Summary
In the field of computer vision and computer graphics, acquiring, modelling and synthesising three-dimensional (3D) human faces has become an active research topic. 3D face reconstruction has attracted widespread attention. Existing image-based 3D face reconstruction methods have two main development directions. Predicting a 3D face model from a single image is another important research area. This method is driven by the prior data of the face, which constructs a 3D model based on a learning method. The 3D deformable model [8], the “shape-from-shading” method [9,10], and the convolutional neural network (CNN) regression [11,12,13,14] are the most common techniques
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