This is the open-source reference implementation of the SIGGRAPH 2021 paper Intersection-free Rigid Body Dynamics.

Overview

Rigid IPC

Build License

Robust, intersection-free, simulations of rigid bodies.

This is the open-source reference implementation of the SIGGRAPH 2021 paper Intersection-free Rigid Body Dynamics.

Files

  • src/: source code
  • cmake/ and CMakeLists.txt: CMake files
  • fixtures/: input scripts to rerun all examples in our paper
  • meshes/: input meshes used by the fixtures
  • tests/: unit-tests
  • tools/: Python and Bash scripts for generating and processing results
  • comparisons/: files used in comparisons with other rigid body simulators
  • python/: Python binding files
  • notebooks/: Jupyter notebooks

Build

To build the project, use the following commands from the root directory of the project:

mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j4

Dependencies

All dependancies are downloaded through CMake depending on the build options. The following libraries are used in this project:

  • IPC Toolkit: common IPC functions
  • Eigen: linear algebra
  • libigl: basic geometry functions, predicates, and viewer
  • TBB: parallelization
  • Tight Inclusion CCD: correct (conservative) continuous collision detection between triangle meshes in 3D
  • spdlog: logging information
  • filib: interval arithmetic
  • Niels Lohmann's JSON: parsing input JSON scenes
  • tinygltf: exporting simulation animation to GLTF format
  • finite-diff: finite difference comparisons
    • Only used by the unit tests and when RIGID_IPC_WITH_DERIVATIVE_CHECK=ON

Optional

Scenes

We take as input a single JSON file that specifies the mesh and initial conditions for each body. The fixtures directory contains example scenes.

Python Bindings

We expose some functionality of Rigid IPC through Python. This is still in development and lacks the ability to script many features available in the full simulator.

To build the Python bindings use the setup.py script:

python setup.py install
Comments
  • cmake fails on Linux Mint 19.3

    cmake fails on Linux Mint 19.3

    Here is what I typed:

    $ mkdir build
    $ cd build
    $ cmake -DCMAKE_BUILD_TYPE=Release ..
    -- GCC >= 4.9 detected, enabling colored diagnostics
    -- Third-party: creating target 'Eigen3::Eigen'
    -- Third-party: creating target 'igl::core'
    -- Creating target: igl::core (igl)
    -- Creating target: igl::opengl (igl_opengl)
    -- Creating target: igl::opengl_glfw (igl_opengl_glfw)
    -- Using X11 for window creation
    -- Creating target: igl::opengl_glfw_imgui (igl_opengl_glfw_imgui)
    -- Creating target: igl::png (igl_png)
    -- Creating target: igl::predicates (igl_predicates)
    -- Third-party: creating target 'nlohmann::json'
    -- Third-party: creating target 'spdlog::spdlog'
    -- Build spdlog: 1.9.0
    -- Build type: Release
    -- Generating install
    -- Third-party: creating target 'finitediff::finitediff'
    -- Third-party: creating targets 'Boost::boost'
    -- Fetching Boost
    -- Fetching Boost - done
    -- Boost found: 1.71.0 /home/glenn/src/github.com/ipc-sim/rigid-ipc/build/_deps/boost-src
    -- Found the following ICU libraries:
    --   uc (required)
    --   dt (required)
    --   i18n (required)
    -- Third-party: creating target 'TBB::tbb'
    -- Third-party: creating target 'tight_inclusion::tight_inclusion'
    -- Tight-Inclusion CCD bottom-level project
    -- GCC >= 4.9 detected, enabling colored diagnostics
    -- Searching for AVX...
    -- Using CPU native flags for AVX optimization:  -march=native
    --   Found AVX 2.0 extensions, using flags:  -march=native -mavx2 -mno-avx512f -mno-avx512pf -mno-avx512er -mno-avx512cd
    -- Using Double Precision Floating Points
    -- Third-party: creating target 'PolyFEM::polysolve'
    [ 11%] Performing download step (git clone) for 'polysolve-populate'
    Cloning into 'polysolve-src'...
    fatal: reference is not a tree: a94e9b8ed8302d4b479533c67419f31addb1e987
    CMake Error at polysolve-subbuild/polysolve-populate-prefix/tmp/polysolve-populate-gitclone.cmake:40 (message):
      Failed to checkout tag: 'a94e9b8ed8302d4b479533c67419f31addb1e987'
    
    
    CMakeFiles/polysolve-populate.dir/build.make:110: recipe for target 'polysolve-populate-prefix/src/polysolve-populate-stamp/polysolve-populate-download' failed
    make[2]: *** [polysolve-populate-prefix/src/polysolve-populate-stamp/polysolve-populate-download] Error 1
    CMakeFiles/Makefile2:94: recipe for target 'CMakeFiles/polysolve-populate.dir/all' failed
    make[1]: *** [CMakeFiles/polysolve-populate.dir/all] Error 2
    Makefile:102: recipe for target 'all' failed
    make: *** [all] Error 2
    
    CMake Error at /usr/local/cmake-3.18.2-Linux-x86_64/share/cmake-3.18/Modules/FetchContent.cmake:987 (message):
      Build step for polysolve failed: 2
    Call Stack (most recent call first):
      /usr/local/cmake-3.18.2-Linux-x86_64/share/cmake-3.18/Modules/FetchContent.cmake:1082:EVAL:2 (__FetchContent_directPopulate)
      /usr/local/cmake-3.18.2-Linux-x86_64/share/cmake-3.18/Modules/FetchContent.cmake:1082 (cmake_language)
      /usr/local/cmake-3.18.2-Linux-x86_64/share/cmake-3.18/Modules/FetchContent.cmake:1125 (FetchContent_Populate)
      cmake/recipes/polysolve.cmake:14 (FetchContent_MakeAvailable)
      CMakeLists.txt:225 (include)
    
    
    -- Configuring incomplete, errors occurred!
    See also "/home/glenn/src/github.com/ipc-sim/rigid-ipc/build/CMakeFiles/CMakeOutput.log".
    See also "/home/glenn/src/github.com/ipc-sim/rigid-ipc/build/CMakeFiles/CMakeError.log".
    

    Any ideas how to fix this? It looks like a bad git reference in polysolve-src, possibly.

    Thank you!

    opened by gmlewis 6
  • Missing files or invalid fixture scripts?

    Missing files or invalid fixture scripts?

    I tried some of the examples in the "fixtures" directory, and the simulator works great! Nice work!

    However, I discovered that there are some examples that don't run either because their mesh files don't exist in the repo or because of some other problem.

    For example, this example is missing its meshes: https://github.com/ipc-sim/rigid-ipc/blob/main/fixtures/3D/mechanisms/expanding-lock-box.json#L11-L57

    This example says [2021-09-30 19:57:28.039] [error] Invalid Json file: https://github.com/ipc-sim/rigid-ipc/blob/main/fixtures/2D/compactor.json

    opened by gmlewis 3
  • Added TimeStepping (WIP)

    Added TimeStepping (WIP)

    Updated clang-format so long chains of parameters are shown one on each line (don't hate me)

    IO

    • Added rigid-body fixtures (NEW FORMAT!)
      • Added rigid-body reader
    • Added json/eigen helper to read/write matrices

    OPT

    • Added is_barrier method (and get/set epsilon) to CollisionConstraint so we don't need to pass the BarrierConstraint to the solver
      • Updated barrier constraint to use them
    • Added accessors on OptimizationProblem for barrier case
      • updated ad-hoc problem too since it was used on unit-tests

    PHYSICS

    • updated RigidBody class to

      • include theta: position is now length 3 (x, y, theta)
      • compute mass and moment of intertia (needed for forces)
      • added position of previous step
      • added differentiable world vertices, and flag to obtain the vertices of the current or previous step.
      • TODO: remove world_displacements, we should use world_vertices instead!
    • Added RigidBodyAssembler (to replace RigidBodyAssembler once finished)

      • init method only computes inmmutable information
      • other methods compute assembly on call

    SIMULATION

    • Added new main file for simulation
    • Added UISimSate and UIMenu for the simulation UI
    • TODO: merge with collision-debugging UI or add export of single problematic step
    opened by panchagil 1
  • Newton fix

    Newton fix

    I updated the Newton solver to fail to gradient descent if the line search fails. This helps the optimization make progress even when the Hessian is ill-conditioned. The next step after this pull request is merged is to add a quasi-Newton solver (e.g. BFGS).

    • Exposed initialization of barrier epsilon in the UI.
    • Separated line search into its own file.
    • Newton method now fails to gradient descent.
    opened by zfergus 1
  • Chain rule refactor

    Chain rule refactor

    Refactor structure of project. Now we have only 4 problems

    • Rigid Body Physics + Distance Barrier Constraint + Barrier Solver

    • Particles Physics + Distance Barrier Constraint + Barrier Solver

    • Rigid Body Physics + Volume Constraint + NCP Solver

    • Particles Physics + Volume Constraint + NCP Solver

    • Remove some base classes that were forcing us to write more functions than neccesary

      • Base OptimizationProblem is gone, now we have some interfaces for the different problems
      • Base CollisionConstraint remainds but implements few methods.
    • Removed exmplicit template instantiation and instead created .tpp files to keep template implementations

    opened by panchagil 0
  • Combined the distance barrier and CCD broad-phases

    Combined the distance barrier and CCD broad-phases

    • Exposed broad-phase in order to get the candidate collisions
    • Modified DistanceBarrier::detectCollisions to first build a common collision candidate set
    • Using this set run the narrow-phase of both the barrier and the CCD
    • TODO: Expose the ev_candidates as a member variable.
    • TODO: Add a is_collision_candidates_frozen flag to cause detectCollisions to not run the broad-phase again.
    opened by zfergus 0
  • Added BFGS and gradient descent solvers

    Added BFGS and gradient descent solvers

    • Added BFGS and GD to barrier solver as inner solvers
    • Needed to move some functionality out of NewtonSolver and into OptimizationSolvers
    • TODO: Move free_dof out of the OptimizationSolver and into the optimization problem with the eval_* functions using free_dof to remove elements.
    opened by zfergus 0
  • Added Rigid Body System Derivatives and  Rigid Body Problem

    Added Rigid Body System Derivatives and Rigid Body Problem

    • Added python notebook to get exact derivatives of RB transformation
    • Moved rigid body to its own file (out of rigid_body_system)
      • Added tests for RB gradient/hessian comparing with exact solutions
    • Added assembly of gradient and hessian on RB-System
      • Added test for RB-System comparing with exact solutions

    Added Rigid Body Problem

    • Added Rigid Body Problem to opt/
    • Implemented Functional, its gradient and hessian
      • tested against finite differences
    • Added tensor util to compute the multiplication of (1x2N) * (2N x 3B x 3B) used by the chain rule.
    opened by panchagil 0
  • Rigid body system

    Rigid body system

    • Moved rigid bodies to physics/ folder
      • added RigidBodySystem that keeps list of RB.
    • Moved solvers to solvers/ folder
    • Removed (a lot of)unused code
    opened by panchagil 0
  • Rigid bodies

    Rigid bodies

    Add UI features for controlling rigid bodies individually

    • Added gradient and hessian of compute_particle_displacements
      • This will be removed later it not used
    • Improved readability by using .homogeneous and .hnormalized
    • Edit buttons half width
    • Rigid body section to control the velocity of each body
    • State method to update the displacements and other fields from the rigid bodies
    opened by zfergus 0
  • Add menu to procedurally generate a chain of n links

    Add menu to procedurally generate a chain of n links

    The menu loads the one-link fixture file and duplicates the link n times. Each link has a scaled displacement, so all links have at least one contact.

    opened by zfergus 0
  • Unknown CMake command

    Unknown CMake command "rigid_ipc_download_project"

    Hi, I git clone the rigid-ipc, and use python build.py to compile the project.

    It gives this error: CMake Error at python/CMakeLists.txt:6 (rigid_ipc_download_project): Unknown CMake command "rigid_ipc_download_project". Call Stack (most recent call first): python/CMakeLists.txt:13 (rigid_ipc_download_pybind11)

    my cmake version is 3.16.3 os: ubuntu 20.04 python: miniconda with python 3.7

    BTW, I also tried with:

    mkdir build
    cd build
    cmake -DCMAKE_BUILD_TYPE=Release ..
    make
    

    This can make with no mistakes.

    I think something wrong with the python part? Any suggestions? Thanks!

    opened by WenqiangX 0
Releases(s2021)
Owner
Incremental Potential Contact code and related projects.
BT-Unet: A-Self-supervised-learning-framework-for-biomedical-image-segmentation-using-Barlow-Twins

BT-Unet: A-Self-supervised-learning-framework-for-biomedical-image-segmentation-using-Barlow-Twins Deep learning has brought most profound contributio

Narinder Singh Punn 12 Dec 04, 2022
Real-time VIBE: Frame by Frame Inference of VIBE (Video Inference for Human Body Pose and Shape Estimation)

Real-time VIBE Inference VIBE frame-by-frame. Overview This is a frame-by-frame inference fork of VIBE at [https://github.com/mkocabas/VIBE]. Usage: i

23 Jul 02, 2022
Code release for "Self-Tuning for Data-Efficient Deep Learning" (ICML 2021)

Self-Tuning for Data-Efficient Deep Learning This repository contains the implementation code for paper: Self-Tuning for Data-Efficient Deep Learning

THUML @ Tsinghua University 101 Dec 11, 2022
Spatial Contrastive Learning for Few-Shot Classification (SCL)

This repo contains the official implementation of Spatial Contrastive Learning for Few-Shot Classification (SCL), which presents of a novel contrastive learning method applied to few-shot image class

Yassine 34 Dec 25, 2022
External Attention Network

Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks paper : https://arxiv.org/abs/2105.02358 Jittor code will come soon

MenghaoGuo 357 Dec 11, 2022
Code Repository for The Kaggle Book, Published by Packt Publishing

The Kaggle Book Data analysis and machine learning for competitive data science Code Repository for The Kaggle Book, Published by Packt Publishing "Lu

Packt 1.6k Jan 07, 2023
A Closer Look at Reference Learning for Fourier Phase Retrieval

A Closer Look at Reference Learning for Fourier Phase Retrieval This repository contains code for our NeurIPS 2021 Workshop on Deep Learning and Inver

Tobias Uelwer 1 Oct 28, 2021
This is the source code for our ICLR2021 paper: Adaptive Universal Generalized PageRank Graph Neural Network.

GPRGNN This is the source code for our ICLR2021 paper: Adaptive Universal Generalized PageRank Graph Neural Network. Hidden state feature extraction i

Jianhao 92 Jan 03, 2023
Pytorch implementation for "Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets" (ECCV 2020 Spotlight)

Distribution-Balanced Loss [Paper] The implementation of our paper Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets (

Tong WU 304 Dec 22, 2022
This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

BiPointNet: Binary Neural Network for Point Clouds Created by Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Li

Haotong Qin 59 Dec 17, 2022
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and reinforcement learning

safe-control-gym Physics-based CartPole and Quadrotor Gym environments (using PyBullet) with symbolic a priori dynamics (using CasADi) for learning-ba

Dynamic Systems Lab 300 Dec 28, 2022
This repo contains research materials released by members of the Google Brain team in Tokyo.

Brain Tokyo Workshop 🧠 🗼 This repo contains research materials released by members of the Google Brain team in Tokyo. Past Projects Weight Agnostic

Google 1.2k Jan 02, 2023
EvoJAX is a scalable, general purpose, hardware-accelerated neuroevolution toolkit

EvoJAX: Hardware-Accelerated Neuroevolution EvoJAX is a scalable, general purpose, hardware-accelerated neuroevolution toolkit. Built on top of the JA

Google 598 Jan 07, 2023
SegNet-like Autoencoders in TensorFlow

SegNet SegNet is a TensorFlow implementation of the segmentation network proposed by Kendall et al., with cool features like strided deconvolution, a

Andrea Azzini 66 Nov 05, 2021
A Tensorfflow implementation of Attend, Infer, Repeat

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models This is an unofficial Tensorflow implementation of Attend, Infear, Repeat (AIR)

Adam Kosiorek 82 May 27, 2022
Official PyTorch implementation of "VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization" (CVPR 2021)

VITON-HD — Official PyTorch Implementation VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization Seunghwan Choi*1, Sunghyun Pa

Seunghwan Choi 250 Jan 06, 2023
Fully Convolutional DenseNets for semantic segmentation.

Introduction This repo contains the code to train and evaluate FC-DenseNets as described in The One Hundred Layers Tiramisu: Fully Convolutional Dense

485 Nov 26, 2022
QICK: Quantum Instrumentation Control Kit

QICK: Quantum Instrumentation Control Kit The QICK is a kit of firmware and software to use the Xilinx RFSoC to control quantum systems. It consists o

81 Dec 15, 2022
The official implementation of the CVPR 2021 paper FAPIS: a Few-shot Anchor-free Part-based Instance Segmenter

FAPIS The official implementation of the CVPR 2021 paper FAPIS: a Few-shot Anchor-free Part-based Instance Segmenter Introduction This repo is primari

Khoi Nguyen 8 Dec 11, 2022
NeuroFind - A solution to the to the Task given by the Oberseminar of Messtechnik Institute of TU Dresden in 2021

NeuroFind A solution to the to the Task given by the Oberseminar of Messtechnik

1 Jan 20, 2022