Ms. LiNan Ren Profile

LiNan Ren

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Proceedings Article | 25 May 2023 Paper

Application and study of virtual reality in the diagnosis system of peritoneal effusion

Jie Chen, Li Nan Ren, Qing Han

Proceedings Volume 12636, 126364F (2023) https://doi.org/10.1117/12.2675155

KEYWORDS: Interpolation, Water, Computer simulations, Particles, Virtual reality, Statistical analysis, Particle systems, Visualization, Computer programming

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The virtual reality-based pneumoperitoneum diagnosis system is an important part of the simulation of laparotomy, and the simulation of pneumoperitoneum is the key to virtual puncture teaching. This paper conducts a test study on the frame rate, the rendering time per frame, the number of triangles and fixed points to be drawn and other related factors during the simulation of pneumoperitoneum. The paper compares the topographic water resources in the standard repository provided by unity and the water resources in the OBI particle system, and addresses the problems and shortcomings of the topographic water in the standard repository, which cannot flow due to gravity and has poor simulation quality, and the OBI particle system, which has low rendering frame rate and long rendering time for simulating water, and proposes a Shader Graph water rendering method based on the Shader Graph, and then simulates the peritoneal fluid through programming, improved rendering frame rate, reduced the number of unnecessary drawn triangles and vertices, and accelerated rendering time.

Proceedings Article | 3 February 2023 Paper

An improved semi-global stereo matching algorithm for 3D reconstruction

GuoDong Song, LiNan Ren

Proceedings Volume 12511, 1251123 (2023) https://doi.org/10.1117/12.2660088

KEYWORDS: Reconstruction algorithms, 3D modeling, Image fusion, Image processing, Algorithms, 3D vision, 3D image reconstruction

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Stereo matching is a key step in 3D reconstruction based on binocular vision. The choice of stereo matching algorithm is directly related to the effect of 3D reconstruction. Aiming at the limitation of the semi-global stereo matching algorithm SGBM, the Census transform relies too much on the center point pixel and is easily disturbed by noise. An improved algorithm based on SGBM is proposed as a stereo matching algorithm in this paper. In the original cost calculation stage, the algorithm replaces the gray value of the original center pixel with the minimum error gray mean value of the multichannel neighborhood to perform the Census operation, and combines the new operation result with the AD cost of the pixel to calculate the initial matching cost. It effectively solves the pixel dependence of the center point and the matching ambiguity problem of a single Census cost in the repeated area. The disparity map is recovered by methods such as multipath cost aggregation and left-right consistency detection after improved cost calculation is completed. This paper uses the Middlebury standard data set to verify the effectiveness of the improved algorithm. Through experimental comparison and analysis, the disparity map generated by the improved algorithm in this paper is effectively improved.

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