Method of acquiring three-dimensional foot shape by using foot shape video and sensor data acquired by smart phone

Abstract

The invention discloses a method of acquiring a three-dimensional foot shape by using a foot shape video and sensor data acquired by a smart phone. The smart phone provided with an IMU sensor is coiled on a naked foot to shoot the foot shape video and corresponding IMU data at 360degrees, and by establishing a factor diagram, a video camera attitude, a three-dimensional point cloud coordinate, the IMU data, a video contour, and other various acquired data are used as factors, and then by defining constraint error equations among the factors and solving the equations, the parameters of the video camera are acquired, and are introduced in a reference model for foot shape reconstruction. The point cloud distribution, the normal vector, the surface curvature, and other information of the reference model are fully used to guide deformation of a reconstructed foot shape, and then under the condition of the reconstructed foot shape being not deviated from a reconstruction point cloud, the reconstructed foot shape is close to a real foot shape as much as possible, and interferences of noise points are reduced, and therefore a process of acquiring a corresponding foot shape three-dimensional grid model conveniently is realized. Acquisition costs of foot shape data are reduced, and accuracy and robustness of parameter calculation are improved.

Description

technical field [0001] The invention relates to a three-dimensional reconstruction method, in particular to a method for obtaining three-dimensional foot shape data based on a smart phone with a camera and a sensor by collecting foot shape RGB video and sensor data and combining with a reference foot shape model in a database. Background technique [0002] In the past two years, there have been more and more studies on the combination of sensors and 3D reconstruction algorithms, resulting in a large number of high-quality 3D reconstruction algorithms, which have rapidly improved the 3D reconstruction technology based on mobile terminals. To summarize the 3D reconstruction algorithms, the 3D reconstruction algorithms based on mobile terminals can be roughly divided into the following categories: 3D reconstruction based on depth map fusion, 3D reconstruction based on IMU and visual fusion, and other sensors based on lighting conditions and laser scanning. Three-dimensional rec...

AI Technical Summary

Problems solved by technology

However, network learning requires a large amount of training data, and the workload of data collection and organization is huge. The reconstruction results depend on data and training. Compared with the reconstruction of vision and IMU fusion, there is not much advantage in error accuracy.

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