Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh

Overview

generate_cloud_points

Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh.

Run

python disp_mesh.py

Or you could use command line arguments: python disp_mesh.py -i data/socks.txt

Triangle Mesh

We provide three tirangle meshes: bunny, armadillo and christmas socks.

Triangle mesh format

numPoints 100
0.1 0.2
....
numTriangles 200
0 1 2
....

Implementation details

  1. Read the triangle mesh with read_obj.py
  2. Find boundary edges using find_boundary.py
  3. Generate random points p in the AABB of triangle mesh
  4. Shoot a ray from p and count how many time the ray intersects with boundary edges
  5. If the number of intersections is even, then the point p is outside the triangle mesh
  6. Otherwise, the point p is inside the triangle mesh
  7. If p is inside the triangle mesh, we add it into the final cloud points field (ti.Vector.field)
Owner
Peng Yu
Peng Yu
PURE: End-to-End Relation Extraction

PURE: End-to-End Relation Extraction This repository contains (PyTorch) code and pre-trained models for PURE (the Princeton University Relation Extrac

Princeton Natural Language Processing 657 Jan 09, 2023
This repository includes the code of the sequence-to-sequence model for discontinuous constituent parsing described in paper Discontinuous Grammar as a Foreign Language.

Discontinuous Grammar as a Foreign Language This repository includes the code of the sequence-to-sequence model for discontinuous constituent parsing

Daniel Fernández-González 2 Apr 07, 2022
Sign Language Transformers (CVPR'20)

Sign Language Transformers (CVPR'20) This repo contains the training and evaluation code for the paper Sign Language Transformers: Sign Language Trans

Necati Cihan Camgoz 164 Dec 30, 2022
PyTorch implementation of the paper: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features

Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features Estimate the noise transition matrix with f-mutual information. This co

<a href=[email protected]"> 1 Jun 05, 2022
Dense Prediction Transformers

Vision Transformers for Dense Prediction This repository contains code and models for our paper: Vision Transformers for Dense Prediction René Ranftl,

Intel ISL (Intel Intelligent Systems Lab) 1.3k Dec 28, 2022
Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF shows significant improvements over baseline fine-tuning without data filtration.

Information Gain Filtration Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF sho

4 Jul 28, 2022
(ICCV 2021) PyTorch implementation of Paper "Progressive Correspondence Pruning by Consensus Learning"

CLNet (ICCV 2021) PyTorch implementation of Paper "Progressive Correspondence Pruning by Consensus Learning" [project page] [paper] Citing CLNet If yo

Chen Zhao 22 Aug 26, 2022
Code release for "MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound"

merlot_reserve Code release for "MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound" MERLOT Reserve (in submission) is a mo

Rowan Zellers 92 Dec 11, 2022
Official Chainer implementation of GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral)

GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral) [Project] [Paper] [Demo] [Related Work: A2RL (for Auto Image Cropping)] [C

Wu Huikai 402 Dec 27, 2022
Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN

Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN Introduction Image super-resolution (SR) is the process of recovering high-resoluti

8 Apr 15, 2022
This repo provides code for QB-Norm (Cross Modal Retrieval with Querybank Normalisation)

This repo provides code for QB-Norm (Cross Modal Retrieval with Querybank Normalisation) Usage example python dynamic_inverted_softmax.py --sims_train

36 Dec 29, 2022
Code for "Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space"

Sparse Steerable Convolution (SS-Conv) Code for "Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and

25 Dec 21, 2022
Relative Human dataset, CVPR 2022

Relative Human (RH) contains multi-person in-the-wild RGB images with rich human annotations, including: Depth layers (DLs): relative depth relationsh

Yu Sun 112 Dec 02, 2022
Code repository for the paper: Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild (ICCV 2021)

Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild Akash Sengupta, Ignas Budvytis, Robert

Akash Sengupta 149 Dec 14, 2022
Pytorch codes for Feature Transfer Learning for Face Recognition with Under-Represented Data

FTLNet_Pytorch Pytorch codes for Feature Transfer Learning for Face Recognition with Under-Represented Data 1. Introduction This repo is an unofficial

1 Nov 04, 2020
PyTorch implementation for paper Neural Marching Cubes.

NMC PyTorch implementation for paper Neural Marching Cubes, Zhiqin Chen, Hao Zhang. Paper | Supplementary Material (to be updated) Citation If you fin

Zhiqin Chen 109 Dec 27, 2022
Adversarial Adaptation with Distillation for BERT Unsupervised Domain Adaptation

Knowledge Distillation for BERT Unsupervised Domain Adaptation Official PyTorch implementation | Paper Abstract A pre-trained language model, BERT, ha

Minho Ryu 29 Nov 30, 2022
HAT: Hierarchical Aggregation Transformers for Person Re-identification

HAT: Hierarchical Aggregation Transformers for Person Re-identification

11 Sep 05, 2022
Code for SALT: Stackelberg Adversarial Regularization, EMNLP 2021.

SALT: Stackelberg Adversarial Regularization Code for Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach, EMNLP 2021. R

Simiao Zuo 10 Jan 10, 2022
PyTorch implementation of Munchausen Reinforcement Learning based on DQN and SAC. Handles discrete and continuous action spaces

Exploring Munchausen Reinforcement Learning This is the project repository of my team in the "Advanced Deep Learning for Robotics" course at TUM. Our

Mohamed Amine Ketata 10 Mar 10, 2022