Source code for "Interactive All-Hex Meshing via Cuboid Decomposition [SIGGRAPH Asia 2021]".

Overview

Interactive All-Hex Meshing via Cuboid Decomposition

teaser Video demonstration

This repository contains an interactive software to the PolyCube-based hex-meshing problem. You can solve hex meshing by playing minecraft!

Features include:

  • a 4-stage interactive pipeline that can robustly generate high-quality hex meshes from an input tetrahedral mesh;
  • extensive user control over each stage, such as editing the voxelized PolyCube, positioning surface vertices, and exploring the trade-off among competing quality metrics;
  • automatic alternatives based on GPU-powered continuous optimization that can run at interactive speed.

It is the original implementation of the SIGGRAPH Asia 2021 paper "Interactive All-Hex Meshing via Cuboid Decomposition" by Lingxiao Li, Paul Zhang, Dmitriy Smirnov, Mazdak Abulnaga, Justin Solomon. Check out our paper for a complete description of our pipeline!

Organization

There are three main components of the project.

  • The geomlib folder contains a standalone C++ library with GPU-based geometric operations including point-triangle projection (in arbitrary dimensions), point-tetrahedron projection (in arbitrary dimensions), point-in-tet-mesh inclusion testing, sampling on a triangular mesh, capable of handling tens of thousands of point queries on large meshes in milliseconds.
  • The vkoo folder contains a standalone object-oriented Vulkan graphics engine that is built based on the official Vulkan samples code with a lot of simplification and modification for the purpose of this project.
  • The hex folder contains the application-specific code for our interactive PolyCube-based hex meshing software, and should be most relevant for learning about the implementation details of our paper.

In addition,

  • results.zip contains the *.h5 project file and the *.mesh output hex mesh file for each model in the Table 2 of the paper. The *.h5 project files can be loaded in our software using File > Open.
  • The assets folder contains a small number of tetrahedral meshes to test on, but you can include your own meshes easily (if you only have triangular meshes, try using TetGen or this to mesh the interior first).
  • The external folder contains additional dependencies that are included in the repo.

Dependencies

Main dependencies that are not included in the repo and should be installed first:

  • CMake
  • CUDA (tested with 11.2, 11.3, 11.4, 11.5) and cuDNN
  • Pytorch C++ frontend (tested with 1.7, 1.8, 1.9, 1.10)
  • Vulkan SDK
  • Python3
  • HDF5

There are additional dependencies in external and should be built correctly with the provided CMake hierarchy:

  • Eigen
  • glfw
  • glm
  • glslang
  • imgui
  • spdlog
  • spirv-cross
  • stb
  • yaml-cpp

Linux Instruction

The instruction is slightly different on various Linux distributions. We have tested on Arch Linux and Ubuntu 20.04. First install all dependencies above using the respective package manager. Then download and unzip Pytorch C++ frontend for Linux (tested with cxx11 ABI) -- it should be under the tab Libtorch > C++/Java > CUDA 11.x. Add Torch_DIR=<unzipped folder> to your environment variable lists (or add your unzipped folder to CMAKE_PREFIX_PATH). Then clone the repo (be sure to use --recursive to clone the submodules as well). Next run the usual cmake/make commands to build target hex in Debug or Release mode:

mkdir -p build/Release
cd build/Release
cmake ../.. -DCMAKE_BUILD_TYPE=Release
make hex -j

This should generate an executable named hex under bin/Release/hex which can be run directly. See CMakeLists.txt for more information.

Windows Instruction

Compiling on Windows is trickier than on Linux. The following procedure has been tested to work on multiple Windows machines.

  • Download and install Visual Studio 2019
  • Download and install the newest CUDA Toolkit (tested with 11.2)
  • Download and install cuDNN for Windows (this amounts to copying a bunch of dll's to the CUDA path)
  • Download and install the newest Vulkan SDK binary for Windows
  • Download and install Python3
  • Download and unzip Pytorch C++ frontend for Windows. Then add TORCH_DIR=<unzipped folder> to your environment variable lists.
  • Download and install HDF5 for Windows
  • In VS2019, install CMake tools, and then build the project following this This should generate an executable under bin/Debug or bin/Release.
Owner
Lingxiao Li
Lingxiao Li
PyTorch implementation of Wide Residual Networks with 1-bit weights by McDonnell (ICLR 2018)

1-bit Wide ResNet PyTorch implementation of training 1-bit Wide ResNets from this paper: Training wide residual networks for deployment using a single

Sergey Zagoruyko 122 Dec 07, 2022
Serving PyTorch 1.0 Models as a Web Server in C++

Serving PyTorch Models in C++ This repository contains various examples to perform inference using PyTorch C++ API. Run git clone https://github.com/W

Onur Kaplan 223 Jan 04, 2023
FairFuzz: AFL extension targeting rare branches

FairFuzz An AFL extension to increase code coverage by targeting rare branches. FairFuzz has a particular advantage on programs with highly nested str

Caroline Lemieux 222 Nov 16, 2022
TensorFlow ROCm port

Documentation TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, a

ROCm Software Platform 622 Jan 09, 2023
This tutorial repository is to introduce the functionality of KGTK to first-time users

Welcome to the KGTK notebook tutorial The goal of this tutorial repository is to introduce the functionality of KGTK to first-time users. The Knowledg

USC ISI I2 58 Dec 21, 2022
Novel Instances Mining with Pseudo-Margin Evaluation for Few-Shot Object Detection

Novel Instances Mining with Pseudo-Margin Evaluation for Few-Shot Object Detection (NimPme) The official implementation of Novel Instances Mining with

12 Sep 08, 2022
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

Alpha Zero General (any game, any framework!) A simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play

Surag Nair 3.1k Jan 05, 2023
I-SECRET: Importance-guided fundus image enhancement via semi-supervised contrastive constraining

I-SECRET This is the implementation of the MICCAI 2021 Paper "I-SECRET: Importance-guided fundus image enhancement via semi-supervised contrastive con

13 Dec 02, 2022
We simulate traveling back in time with a modern camera to rephotograph famous historical subjects.

[SIGGRAPH Asia 2021] Time-Travel Rephotography [Project Website] Many historical people were only ever captured by old, faded, black and white photos,

298 Jan 02, 2023
Gin provides a lightweight configuration framework for Python

Gin Config Authors: Dan Holtmann-Rice, Sergio Guadarrama, Nathan Silberman Contributors: Oscar Ramirez, Marek Fiser Gin provides a lightweight configu

Google 1.7k Jan 03, 2023
Code for the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge.

Open Sesame This repository contains the code for the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge. Credits We built the project on t

9 Jul 24, 2022
Official Code for VideoLT: Large-scale Long-tailed Video Recognition (ICCV 2021)

Pytorch Code for VideoLT [Website][Paper] Updates [10/29/2021] Features uploaded to Google Drive, for access please send us an e-mail: zhangxing18 at

Skye 26 Sep 18, 2022
Minecraft Hack Detection With Python

Minecraft Hack Detection An attempt to try and use crowd sourced replays to find

Kuleen Sasse 3 Mar 26, 2022
Real-time Object Detection for Streaming Perception, CVPR 2022

StreamYOLO Real-time Object Detection for Streaming Perception Jinrong Yang, Songtao Liu, Zeming Li, Xiaoping Li, Sun Jian Real-time Object Detection

Jinrong Yang 237 Dec 27, 2022
This repo is a PyTorch implementation for Paper "Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds"

Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds This repository is a PyTorch implementation for paper: Uns

Kaizhi Yang 42 Dec 09, 2022
E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation

E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation E2EC: An End-to-End Contour-based Method for High-Quality H

zhangtao 146 Dec 29, 2022
PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

StyleSpeech - PyTorch Implementation PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Status (2021.06.13

Keon Lee 140 Dec 21, 2022
A GUI to automatically create a TOPAS-readable MLC simulation file

Python script to create a TOPAS-readable simulation file descriring a Multi-Leaf-Collimator. Builds the MLC using the data from a 3D .stl file.

Sebastian Schäfer 0 Jun 19, 2022
Codebase of deep learning models for inferring stability of mRNA molecules

Kaggle OpenVaccine Models Codebase of deep learning models for inferring stability of mRNA molecules, corresponding to the Kaggle Open Vaccine Challen

Eternagame 40 Dec 29, 2022
Project repo for Learning Category-Specific Mesh Reconstruction from Image Collections

Learning Category-Specific Mesh Reconstruction from Image Collections Angjoo Kanazawa*, Shubham Tulsiani*, Alexei A. Efros, Jitendra Malik University

438 Dec 22, 2022