Mesh processing for improved perceptual quality of 3D printed relief

Making arts and crafts is an essential application of 3D printing. However, typically, 3D printers have limited resolution; thus, the perceptual quality of the result is always low, mainly when the input mesh is a relief. To address this problem using existing 3D printing technology, we only operate the shape of the input triangle mesh. To improve the perceptual quality of a 3D printed product, we propose an integrated mesh processing that comprises feature extraction, 3D print preview, feature preservation test, and shape enhancement. The proposed method can identify and enlarge features that need to be enhanced without large-scale deformation. Besides, to improve ease of use, intermediate processes are visualized via user interfaces. To evaluate the proposed method, the processed triangle meshes are 3D printed. The effectiveness of the proposed approach is confirmed by comparing photographs of the original 3D prints and the enhanced 3D prints.

RAPID CREATION OF VEHICLE LINE-UPS BY EIGENSPACE PROJECTIONS FOR STYLE TRANSFER

In product development, an automated generation of shape variations enables a rapid assessment of potentially appealing design directions. We present a framework for computing a product line-up of automotive body shapes based on spectral methods for mesh processing. We calculate the eigenspace projections of 3D vehicle meshes and identify the relevant style as well as content components based on the eigenvalues. The style of a model is then transferred to arbitrary prototype content car shapes, which allows for a rapid portfolio generation of various car types with minimal user interaction.

GAMer 2: A System for 3D Mesh Processing of Cellular Electron Micrographs

ObjectiveRecent advances in electron microscopy have, for the first time, enabled imaging of single cells in 3D at a nanometer length scale resolution. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. However, this will require a system for going from EM images to 3D volume meshes which can be used in finite element simulations.MethodsIn this paper, we develop an end-to-end pipeline for this task by adapting and extending computer graphics mesh processing and smoothing algorithms. Our workflow makes use of our recently rewritten mesh processing software, GAMer 2, which implements several mesh conditioning algorithms and serves as a platform to connect different pipeline steps.ResultsWe apply this pipeline to a series of electron micrographs of dendrite morphology explored at three different length scales and show that the resultant meshes are suitable for finite element simulations.ConclusionOur pipeline, which consists of free and open-source community driven tools, is a step towards routine physical simulations of biological processes in realistic geometries.SignificanceWe posit that a new frontier at the intersection of computational technologies and single cell biology is now open. Innovations in algorithms to reconstruct and simulate cellular length scale phenomena based on emerging structural data will enable realistic physical models and advance discovery.

Distributed Combinatorial Maps for Parallel Mesh Processing

We propose a new strategy for the parallelization of mesh processing algorithms. Our main contribution is the definition of distributed combinatorial maps (called n-dmaps), which allow us to represent the topology of big meshes by splitting them into independent parts. Our mathematical definition ensures the global consistency of the meshes at their interfaces. Thus, an n-dmap can be used to represent a mesh, to traverse it, or to modify it by using different mesh processing algorithms. Moreover, an nD mesh with a huge number of elements can be considered, which is not possible with a sequential approach and a regular data structure. We illustrate the interest of our solution by presenting a parallel adaptive subdivision method of a 3D hexahedral mesh, implemented in a distributed version. We report space and time performance results that show the interest of our approach for parallel processing of huge meshes.