CuMesh is a GPU-accelerated library for high-performance 3D geometry processing within the PyTorch ecosystem. It provides efficient CUDA implementations for mesh cleaning, decimation, remeshing, and UV unwrapping. The library centers around the CuMesh class which manages mesh data on GPU and exposes operations through PyTorch tensor interfaces. Key components include a QEM-based mesh simplification algorithm, narrow-band dual contouring remeshing, accelerated mesh clustering for UV unwrapping, and comprehensive mesh connectivity queries. The implementation demonstrates strong GPU programming practices with proper memory management, error checking, and efficient use of primitives like CUB for parallel operations.
This repository is a comprehensive C++ library and suite of command-line tools for mesh processing research, originally developed by Hugues Hoppe at Microsoft Research. It implements algorithms from several SIGGRAPH papers (1992–2003), including surface reconstruction from unorganized points, mesh optimization, progressive meshes, view-dependent refinement, subdivision surface fitting, spherical parameterization, and gradient-domain video looping. The core library, libHh, provides a rich set of reusable data structures (e.g., fixed-size vectors, D-dimensional grids, half-edge meshes, progressive mesh representations) and algorithms (spatial search, least-squares solvers, multigrid, image filtering, parallel patterns). The tools are designed as stream-oriented filters that read from stdin and write to stdout, enabling Unix-style pipelines for complex processing workflows. An interactive OpenGL viewer (G3dOGL) supports real-time rendering with level-of-detail and geomorph transitions. The build system supports multiple platforms (Windows, Linux, macOS via Cygwin/MinGW) using GNU make and Visual Studio, with optional dependencies on libpng, libjpeg, and ffmpeg for media I/O. The codebase is well-tested with a comprehensive suite of unit and integration tests, and includes numerous demo scripts that reproduce published research results.