Implicit surfaces from polygon soup with compactly supported radial basis functions

Shi

et al. 2020

IJGI

The digital elevation model (DEM) generates a digital simulation of ground terrain in a certain range with the usage of 3D point cloud data. It is an important source of spatial modeling information. Due to various reasons, however, the generated DEM has data holes. Based on the algorithm of deep learning, this paper aims to train a deep generation model (DGM) to complete the DEM void filling task. A certain amount of DEM data and a randomly generated mask are taken as network inputs, along which the reconstruction loss and generative adversarial network (GAN) loss are used to assist network training, so as to perceive the overall known elevation information, in combination with the contextual attention layer, and generate data with reliability to fill the void areas. The experimental results have managed to show that this method has good feature expression and reconstruction accuracy in DEM void filling, which has been proven to be better than that illustrated by the traditional interpolation method.

Section: Introductionmentioning

confidence: 99%

DEM Void Filling Based on Context Attention Generation Model

Shi

et al. 2020

IJGI

Medical Data and Mathematically Modeled Implicit Surface Real-Rime Visualization in Web Browsers

2021

Int. J. Image Grap.

Raycasting can display volumetric medical data in fine details and reveal crucial inner imaging information, while implicit surface is able to effectively model complex objects with high flexibility, combining these two rendering modalities together will provide comprehensive information of the scene and has wide applications in surgical simulation, image-guided intervention, and medical training. However, medical data rendering is based on texture depth at every sampling point, while mathematically modeled implicit surfaces do not have geometric information in texture space. It is a challenging task to visualize both physical scalar data and virtual implicit surfaces simultaneously. To address this issue, in this paper, we present a new dual-casting ray-based double modality data rendering algorithm and web-based software platform to visualize volumetric medical data and implicit surface in the same browser. The algorithm runs on graphics processing unit and casts two virtual rays from camera to each pixel on the display panel, where one ray travels through the mathematically defined scene for implicit surface rendering and the other one passes the 3D texture space for volumetric data visualization. The proposed algorithm can detect voxel depth information and algebraic surface models along each casting ray and dynamically enhance the visualized dual-modality data with the improved lighting model and transparency adjustment function. Moreover, auxiliary innovative techniques are also presented to enhance the shading and rendering features of interest. Our software platform can seamlessly visualize volumetric medical data and implicit surfaces in the same web browser over Internet.

Integral Parametric Surface Reconstruction Method Based on Differential Manifold and Non-Uniform Rational B-Spline

Han

Xie

2023

J Flow Vis Image Proc

A reconstruction method based on differential manifold and non-uniform rational B-spline is proposed to improve the reconstruction performance of massive point complex surface models. First, this method is simplified based on the Hausdorff distance of feature points. Second, in order to completely reconstruct the model and avoid the complex splicing of non-uniform rational B-spline, the regular domain of non-uniform rational B-spline is extended to differential manifolds, and the basis functions of the control vertices are established by using the shortest distance. Finally, the element decomposition of the differential manifold is calculated through the normalized basis functions, composite element decomposition, and control vertices to achieve the final surface model. The experiment results show that the proposed method has high reconstruction efficiency and accuracy for arbitrary topological surfaces.