A tight inclusion function for continuous collision detection

Related tags

Deep Learning collision-detection ccd
Overview

Tight-Inclusion Continuous Collision Detection

Build

A conservative Continuous Collision Detection (CCD) method with support for minimum separation.

You can read more about this work in our ACM Transactions on Graphics paper:

"A Large Scale Benchmark and an Inclusion-Based Algorithm forContinuous Collision Detection"

@article{Wang:2021:Benchmark,
    title   = {A Large Scale Benchmark and an Inclusion-Based Algorithm for Continuous Collision Detection},
    author  = {Bolun Wang and Zachary Ferguson and Teseo Schneider and Xin Jiang and Marco Attene and Daniele Panozzo},
    year    = 2021,
    journal = {ACM Transactions on Graphics}
}

Compiling Instruction

To compile the code, first make sure CMake is installed.

To build the library on Linux or macOS:

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

Then you can run a CCD example:

./Tight_Inclusion_bin

We also provide you an example to run the Sample Queries using our CCD method. You may need to install gmp before compiling the code. Then set the CMake option TIGHT_INCLUSION_WITH_TESTS as ON when compiling:

cmake ../ -DCMAKE_BUILD_TYPE=Release -DTIGHT_INCLUSION_WITH_TESTS=ON
make

Then you can run ./Tight_Inclusion_bin to test the handcrafted queries in the Sample Queries.

Usage

Include #include <tight_inclusion/inclusion_ccd.hpp>

To check edge-edge ccd, use bool inclusion_ccd::edgeEdgeCCD_double();

To check vertex-face ccd, use bool inclusion_ccd::vertexFaceCCD_double();

πŸ’‘ If collision is detected, the ccd function will return true, otherwise, the ccd function will return false. Since our method is CONSERVATIVE, if the returned result is false, we guarantee that there is no collision happens. If the result is true, it is possible that there is no collision but we falsely report a collision, but we can guarantee that this happens only if the minimal distance between the two primitives in this time step is no larger than tolerance + ms + err. We wil explain these parameters below.

For both edge-edge ccd and vertex-face ccd, the input CCD query is presented by 8 vertices which are in the format of Eigen::Vector3d. Please read our code in tight_inclusion/inclusion_ccd.hpp for the correct input order of the vertices.

Beside the input vertices, there are some input and output parameters for users to tune the performace or to get more CCD information. Here is a list of the explanations of the parameters:

input:
    err                 The numerical filters of the x, y and z coordinates. It measures the errors introduced by floating-point calculation when solving inclusion functions.
    ms                  Minimum separation distance no less than 0. It guarantees that collision will be reported if the distance between the two primitives is less than ms.
    tolerance           User-specific solving precision. It is the target maximal x, y, and z length of the inclusion function. We suggest the user to set it as 1e-6.
    t_max               The time range [0, t_max] where we detect collisions. Since the input query implies the motion in time range [0, 1], t_max should no larger than 1.
    max_itr             The parameter to enable early termination of the algorithm. If you set max_itr < 0, early termination will be disabled, but this may cause longer runtime. We suggest to set max_itr = 1e6.
    CCD_TYPE            The parameter to choose collision detection algorithms. By default CCD_TYPE = 1. If set CCD_TYPE = 0, the code will switch to a naive conservative CCD algorithm, but lack of our advanced features. 
    
output:
    toi                 Time of impact. If multiple collisions happen in this time step, it will return the earlist collision time. If there is no collision, the returned toi value will be std::numeric_limits<double>::infinity().
    output_tolerance    The real solving precision. If early termination is enabled, the solving precision may not reach the target precision. This parameter will return the real solving precision when the code is terminated.

Tips

πŸ’‘ The input parameter err is crucial to guarantee our algorithm to be a conservative method not affected by floating point rounding errors. To run a single query, you can set err = {{-1, -1, -1}} to enable a sub-function to calculate the real numerical filters when solving CCD. If you are integrating our CCD in simulators, you need to:

  • Include the headler: #include <tight_inclusion/interval_root_finder.hpp>.
  • Call std::array<double, 3> err_vf = inclusion_ccd::get_numerical_error() and std::array<double, 3> err_ee = inclusion_ccd::get_numerical_error()
  • Use the parameter err_ee each time you call bool inclusion_ccd::edgeEdgeCCD_double() and err_vf when you call bool inclusion_ccd::vertexFaceCCD_double().

The parameters for function inclusion_ccd::get_numerical_error() is explained below:

input:
    vertices            Vertices of the Axies-Aligned-Bounding-Box of the simulation scene. Before you run the simulation, you need to conservatively estimate the Axies-Aligned-Bounding-Box in which the meshes will located during the whole simulation process, and the vertices should be the corners of the AABB.
    check_vf            A boolean instruction showing if you are checking vertex-face or edge-edge CCD.
    using_minimum_separation    A boolean instruction. If you are using minimum-separation CCD (the input parameter ms > 0), please set it as true.

πŸ’‘ For some simulators which use non-zero minimum separation distance (ms > 0) to make sure intersection-free for each time-step, we have a CMake option TIGHT_INCLUSION_WITH_NO_ZERO_TOI to avoid the returned collision time toi is 0. You need to set TIGHT_INCLUSION_WITH_NO_ZERO_TOI as ON when compiling by: cmake ../ -DCMAKE_BUILD_TYPE=Release -DTIGHT_INCLUSION_WITH_NO_ZERO_TOI=ON. Then when you use the CCD functions, the code will continue the refinement in higher precision if the output toi is 0 under the given tolerance. So, the eventually toi will not be 0.

To have a better understand, or to get more details of our Tight-Inclusion CCD algorithm, please refer to our paper.

You might also like...
Continuous Query Decomposition for Complex Query Answering in Incomplete Knowledge Graphs

Continuous Query Decomposition This repository contains the official implementation for our ICLR 2021 (Oral) paper, Complex Query Answering with Neura

UCL Natural Language Processing 71 Dec 29, 2022
On the model-based stochastic value gradient for continuous reinforcement learning

On the model-based stochastic value gradient for continuous reinforcement learning This repository is by Brandon Amos, Samuel Stanton, Denis Yarats, a

Facebook Research 46 Dec 15, 2022
Continuous Diffusion Graph Neural Network

We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE.

Twitter Research 227 Jan 5, 2023
PyTorch implementation for MINE: Continuous-Depth MPI with Neural Radiance Fields
PyTorch implementation for MINE: Continuous-Depth MPI with Neural Radiance Fields

MINE: Continuous-Depth MPI with Neural Radiance Fields Project Page | Video PyTorch implementation for our ICCV 2021 paper. MINE: Towards Continuous D

Zijian Feng 325 Dec 29, 2022
This repository contains the source code and data for reproducing results of Deep Continuous Clustering paper
This repository contains the source code and data for reproducing results of Deep Continuous Clustering paper

Deep Continuous Clustering Introduction This is a Pytorch implementation of the DCC algorithms presented in the following paper (paper): Sohil Atul Sh

Sohil Shah 197 Nov 29, 2022
This is the codebase for the ICLR 2021 paper Trajectory Prediction using Equivariant Continuous Convolution
This is the codebase for the ICLR 2021 paper Trajectory Prediction using Equivariant Continuous Convolution

Trajectory Prediction using Equivariant Continuous Convolution (ECCO) This is the codebase for the ICLR 2021 paper Trajectory Prediction using Equivar

Spatiotemporal Machine Learning 45 Jul 22, 2022
PyTorch implementation for Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition.

Stochastic CSLR This is the PyTorch implementation for the ECCV 2020 paper: Stochastic Fine-grained Labeling of Multi-state Sign Glosses for Continuou

Zhe Niu 28 Dec 19, 2022
Code for the ICASSP-2021 paper: Continuous Speech Separation with Conformer.

Continuous Speech Separation with Conformer Introduction We examine the use of the Conformer architecture for continuous speech separation. Conformer

Sanyuan Chen (ι™ˆδΈ‰ε…ƒ) 81 Nov 28, 2022
A clean and robust Pytorch implementation of PPO on continuous action space.
A clean and robust Pytorch implementation of PPO on continuous action space.

PPO-Continuous-Pytorch I found the current implementation of PPO on continuous action space is whether somewhat complicated or not stable. And this is

XinJingHao 56 Dec 16, 2022
Comments
  • Improved No Zero ToI

    Improved No Zero ToI

    • I changed the options for avoiding a zero time of impact by making the option a parameter to the CCD functions.
    • I also changed from a recursive algorithm to an iterative one with a maximum number of iterations.
    opened by zfergus 2
  • a case returned 0 TOI

    a case returned 0 TOI

    For the PT CCD case below, TICCD returned 0 TOI.

    3.5000000000e-01 0.0000000000e+00 3.5000000000e-01
3.4604643638e-01 2.7847887896e-03 3.4658121160e-01
3.5000819263e-01 -1.5763377304e-06 3.5001214242e-01
3.4903882032e-01 -1.5866575093e-03 3.4560164356e-01
0.0000000000e+00 0.0000000000e+00 0.0000000000e+00
1.0120300789e-07 -1.4009994248e-04 -8.5653091896e-05
-9.2976239634e-07 1.4361165221e-06 5.5168830145e-07
1.1649271683e-04 -3.6172565398e-05 -4.7128237917e-05

    The format is

    node_x node_y node_z
traingle_node0_x traingle_node0_y traingle_node0_z
traingle_node1_x traingle_node1_y traingle_node1_z
traingle_node2_x traingle_node2_y traingle_node2_z
node_displacement_x node_displacement_y node_displacement_z
traingle_node0_displacement_x traingle_node0_displacement_y traingle_node0_displacement_z
traingle_node1_displacement_x traingle_node1_displacement_y traingle_node1_displacement_z
traingle_node2_displacement_x traingle_node2_displacement_y traingle_node2_displacement_z

    I used the TICCD from the CCDWrapper repo (latest commit), and I called it like

    template <class T>
bool Point_Triangle_CCD_TI(
    const Eigen::Matrix<T, 3, 1>& p, 
    const Eigen::Matrix<T, 3, 1>& t0, 
    const Eigen::Matrix<T, 3, 1>& t1,
    const Eigen::Matrix<T, 3, 1>& t2,
    const Eigen::Matrix<T, 3, 1>& dp, 
    const Eigen::Matrix<T, 3, 1>& dt0, 
    const Eigen::Matrix<T, 3, 1>& dt1,
    const Eigen::Matrix<T, 3, 1>& dt2,
    T eta, T thickness, T& toc)
{
    T toc_prev = toc;
    T output_tolerance;

    T dist2_cur;
    Point_Triangle_Distance_Unclassified(p, t0, t1, t2, dist2_cur);
    T dist_cur = std::sqrt(dist2_cur);
    if (inclusion_ccd::vertexFaceCCD_double(p, t0, t1, t2,
        p + dp, t0 + dt0, t1 + dt1, t2 + dt2, { { -1, 0, 0 } },
        eta * (dist2_cur - thickness * thickness) / (dist_cur + thickness) + thickness,
        toc, 1e-6, toc_prev, 1e6, output_tolerance, 1))
    {
        if (toc < 1.0e-6) {
            std::cout << "PT CCD tiny!" << std::endl;
            if (inclusion_ccd::vertexFaceCCD_double(p, t0, t1, t2,
                p + dp, t0 + dt0, t1 + dt1, t2 + dt2, { { -1, 0, 0 } },
                thickness, toc, 1e-6, toc_prev, 1e6, output_tolerance, 1))
            {
                toc *= (1.0 - eta);
                return true;
            }
            else {
                return false;
            }
        }
        return true;
    }
    return false;
}

printf("TICCD:\n");
largestAlpha = 1;
tstart = clock();
if (Point_Triangle_CCD_TI(x[0], x[1], x[2], x[3], x[4], x[5], x[6], x[7], 0.1, 0.0, largestAlpha)) {
    printf("%le\n", largestAlpha);
}
else {
    printf("no collision\n");
}
printf("%.1lems\n", double(clock() - tstart) / CLOCKS_PER_SEC * 1000);

    The output is

    TICCD:
PT CCD tiny!
0.000000e+00
1.9e+00ms

    However I implemented a simpler version of TICCD according to the paper as

    template <class T>
void Point_Triangle_Distance_Vector_Unclassified(
    const Eigen::Matrix<T, 3, 1>& p, 
    const Eigen::Matrix<T, 3, 1>& t0, 
    const Eigen::Matrix<T, 3, 1>& t1,
    const Eigen::Matrix<T, 3, 1>& t2,
    const Eigen::Matrix<T, 3, 1>& dp, 
    const Eigen::Matrix<T, 3, 1>& dt0, 
    const Eigen::Matrix<T, 3, 1>& dt1,
    const Eigen::Matrix<T, 3, 1>& dt2,
    T t, T lambda, T beta,
    Eigen::Matrix<T, 3, 1>& distVec)
{
    const Eigen::Matrix<T, 3, 1> tp = (1 - lambda - beta) * t0 + lambda * t1 + beta * t2;
    const Eigen::Matrix<T, 3, 1> dtp = (1 - lambda - beta) * dt0 + lambda * dt1 + beta * dt2;
    distVec = p + t * dp - (tp + t * dtp);
}

template <class T>
bool Point_Triangle_CheckInterval_Unclassified(
    const Eigen::Matrix<T, 3, 1>& p, 
    const Eigen::Matrix<T, 3, 1>& t0, 
    const Eigen::Matrix<T, 3, 1>& t1,
    const Eigen::Matrix<T, 3, 1>& t2,
    const Eigen::Matrix<T, 3, 1>& dp, 
    const Eigen::Matrix<T, 3, 1>& dt0, 
    const Eigen::Matrix<T, 3, 1>& dt1,
    const Eigen::Matrix<T, 3, 1>& dt2,
    const std::array<T, 6>& interval,
    T gap)
{
    Eigen::Matrix<T, 3, 1> distVecMax, distVecMin;
    distVecMax.setConstant(-2 * gap - 1);
    distVecMin.setConstant(2 * gap + 1);
    for (int t = 0; t < 2; ++t) {
        for (int lambda = 0; lambda < 2; ++lambda) {
            for (int beta = 0; beta < 2; ++beta) {
                if (lambda == 1 && beta == 1) {
                    continue;
                }
                Eigen::Matrix<T, 3, 1> distVec;
                Point_Triangle_Distance_Vector_Unclassified(p, t0, t1, t2, dp, dt0, dt1, dt2, 
                    interval[t], interval[2 + lambda], interval[4 + beta], distVec);
                distVecMax = distVecMax.array().max(distVec.array());
                distVecMin = distVecMin.array().min(distVec.array());
            }
        }
    }
    return (distVecMax.array() >= -gap).all() && (distVecMin.array() <= gap).all();
}
template <class T>
bool Point_Triangle_TICCD(
    const Eigen::Matrix<T, 3, 1>& p, 
    const Eigen::Matrix<T, 3, 1>& t0, 
    const Eigen::Matrix<T, 3, 1>& t1,
    const Eigen::Matrix<T, 3, 1>& t2,
    Eigen::Matrix<T, 3, 1> dp, 
    Eigen::Matrix<T, 3, 1> dt0, 
    Eigen::Matrix<T, 3, 1> dt1,
    Eigen::Matrix<T, 3, 1> dt2,
    T eta, T thickness, T& toc)
{
    T dist2_cur;
    Point_Triangle_Distance_Unclassified(p, t0, t1, t2, dist2_cur);
    T dist_cur = std::sqrt(dist2_cur);
    T gap = eta * (dist2_cur - thickness * thickness) / (dist_cur + thickness);

    T tTol = 1e-3;

    std::vector<std::array<T, 6>> roots;
    std::deque<std::array<T, 6>> intervals;
    intervals.push_back({0, toc, 0, 1, 0, 1});
    int iterAmt = 0;
    while (!intervals.empty()) {
        ++iterAmt;

        std::array<T, 6> curIV = intervals.front();
        intervals.pop_front();

        // check
        if (Point_Triangle_CheckInterval_Unclassified(p, t0, t1, t2, dp, dt0, dt1, dt2, curIV, gap)) {
            if (curIV[0] && curIV[1] - curIV[0] < tTol) {
                // root found within tTol
                roots.emplace_back(curIV);
            }
            else {
                // split interval and push back
                std::vector<T> intervalLen({curIV[1] - curIV[0], curIV[3] - curIV[2], curIV[5] - curIV[4]});
                switch (std::max_element(intervalLen.begin(), intervalLen.end()) - intervalLen.begin()) {
                case 0:
                    intervals.push_back({curIV[0], (curIV[1] + curIV[0]) / 2, curIV[2], curIV[3], curIV[4], curIV[5]});
                    intervals.push_back({(curIV[1] + curIV[0]) / 2, curIV[1], curIV[2], curIV[3], curIV[4], curIV[5]});
                    break;

                case 1:
                    intervals.push_back({curIV[0], curIV[1], curIV[2], (curIV[2] + curIV[3]) / 2, curIV[4], curIV[5]});
                    intervals.push_back({curIV[0], curIV[1], (curIV[2] + curIV[3]) / 2, curIV[3], curIV[4], curIV[5]});
                    break;

                case 2:
                    intervals.push_back({curIV[0], curIV[1], curIV[2], curIV[3], curIV[4], (curIV[4] + curIV[5]) / 2});
                    intervals.push_back({curIV[0], curIV[1], curIV[2], curIV[3], (curIV[4] + curIV[5]) / 2, curIV[5]});
                    break;
                }
            }
        }
    }
    
    if (roots.empty()) {
        printf("TICCD PT converged with %d iters\n", iterAmt);
        return false;
    }
    else {
        for (const auto& rI : roots) {
            if (toc > rI[0]) {
                toc = rI[0];
            }
        }
        printf("TICCD PT converged with %d iters\n", iterAmt);
        return true;
    }
}

    and it kinds of worked on this case and output

    TICCD PT converged with 6143 iters
4.882812e-04
5.3e-01ms

    which with tighter convergence tolerance might be able to reach the ground truth solution -- no collision.

    In my implementation, I didn't include the epsilons in formula (5) in the paper. Is it the main cause here?

    opened by liminchen 2
  • Refactored and cleaned up code

    Refactored and cleaned up code

    I tested and timed it on the entire dataset. I get 0 FN, a couple of fewer FP on the handcrafted data, and the timing is a little faster on the simulation and faster on handcrafted.

    opened by zfergus 0
Releases(v1.0.2)
Owner
Continuous Collision Detection
Code for continuous collision detection methods bechmarked in "A Large Scale Benchmark and an Inclusion-Based Algorithm for Continuous Collision Detection"
Continuous Collision Detection
GitHub
Code accompanying the paper "How Tight Can PAC-Bayes be in the Small Data Regime?"

How Tight Can PAC-Bayes be in the Small Data Regime? This is the code to reproduce all experiments for the following paper: @inproceedings{Foong:2021:

null 5 Dec 21, 2021
Collision risk estimation using stochastic motion models

collision_risk_estimation Collision risk estimation using stochastic motion models. This is a new approach, based on stochastic models, to predict the

Unmesh 7 Jun 26, 2022
MLOps will help you to understand how to build a Continuous Integration and Continuous Delivery pipeline for an ML/AI project.

page_type languages products description sample python azure azure-machine-learning-service azure-devops Code which demonstrates how to set up and ope

null 1 Nov 1, 2021
Learning Continuous Image Representation with Local Implicit Image Function

LIIF This repository contains the official implementation for LIIF introduced in the following paper: Learning Continuous Image Representation with Lo

Yinbo Chen 1k Dec 25, 2022
Continuous Security Group Rule Change Detection & Response at scale

Introduction Get notified of Security Group Changes across all AWS Accounts & Regions in an AWS Organization, with the ability to respond/revert those

Raajhesh Kannaa Chidambaram 3 Aug 13, 2022
Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.

CSE-Autoloss Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models

Peidong Liu(εˆ˜ζ²›δΈœ) 54 Dec 17, 2022
MiraiML: asynchronous, autonomous and continuous Machine Learning in Python

MiraiML Mirai: future in japanese. MiraiML is an asynchronous engine for continuous & autonomous machine learning, built for real-time usage. Usage In

Arthur Paulino 25 Jul 27, 2022
[CVPR'21] FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space by Quande Liu, Cheng Chen, Ji

Quande Liu 178 Jan 6, 2023
Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, Daniel Silva, Andrew McCallum, Amr Ahmed. KDD 2019.

gHHC Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, D

Nicholas Monath 35 Nov 16, 2022
[ICML 2020] Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

PG-MORL This repository contains the implementation for the paper Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Contro

MIT Graphics Group 65 Jan 7, 2023