Point cloud processing tool library.

Overview

Point Cloud ToolBox

This point cloud processing tool library can be used to process point clouds, 3d meshes, and voxels.

Environment

python 3.7.5

Dependent packages:
- openmesh==1.1.6
- open3d==0.13.0
- plyfile==0.7.4
- numpy==1.21.0
- vtk==8.2.0
- python-pcl==0.3.0rc1

How to install the environment:
# pip install requirements.txt

Todo

Point Cloud

  • format conversion (pointcloud->pointcloud)
    • pcd -> xyz | pts | txt | csv | ply
    • las -> pcd | xyz | pts | ply | txt | csv
    • ply -> pcd | xyz | pts | txt | csv
    • xyz -> pcd | pts | ply | txt | csv
    • pts -> pcd | xyz | ply | txt | csv
    • txt -> pcd | ply | xyz | pts
  • Calculate the surface area and volume of 3D Mesh
  • Point cloud voxelization
  • downsampling
    • farthest point sampling(FPS)
    • random sampling
    • uniform sampling
    • voxel sampling
  • upsampling
  • filtering
    • PassThrough Filter
    • VoxelGrid Filter
    • project_inliers Filter
    • remove_outliers Filter
    • statistical_removal Filter
  • registration
  • 3D reconstruction
  • visualization

3d Mesh

  • format conversion (mesh->mesh)
    • ply -> obj | stl | off
    • obj -> ply | off | stl
    • off -> ply | obj | stl
  • down sampling into point cloud
    • poisson disk sampling
    • uniform sampling
  • mesh filtering
    • Taubin filter
    • Laplacian smooth
    • simple neighbour average
  • mesh voxelization
  • mesh subdivision
  • 3d mesh visualization

voxel

  • visualization

Use

You can find command in run.sh.

To be continued...

Owner
ZhangXinyun
ZhangXinyun
​ This is the Pytorch implementation of Progressive Attentional Manifold Alignment.

PAMA This is the Pytorch implementation of Progressive Attentional Manifold Alignment. Requirements python 3.6 pytorch 1.2.0+ PIL, numpy, matplotlib C

98 Nov 15, 2022
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)

S2-BNN (Self-supervised Binary Neural Networks Using Distillation Loss) This is the official pytorch implementation of our paper: "S2-BNN: Bridging th

Zhiqiang Shen 52 Dec 24, 2022
Oscar and VinVL

Oscar: Object-Semantics Aligned Pre-training for Vision-and-Language Tasks VinVL: Revisiting Visual Representations in Vision-Language Models Updates

Microsoft 938 Dec 26, 2022
Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques

Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques This repository is derived from the NMTGMinor

Tu Anh Dinh 1 Sep 07, 2022
2D&3D human pose estimation

Human Pose Estimation Papers [CVPR 2016] - 201511 [IJCAI 2016] - 201602 Other Action Recognition with Joints-Pooled 3D Deep Convolutional Descriptors

133 Jan 02, 2023
[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis

Focal Frequency Loss - Official PyTorch Implementation This repository provides the official PyTorch implementation for the following paper: Focal Fre

Liming Jiang 460 Jan 04, 2023
Hydra: an Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems

Hydra: An Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems Paper Finding Semantic Bugs in File Systems with an Extensible Fuzzin

gts3.org (<a href=[email protected])"> 129 Dec 15, 2022
An implementation of the BADGE batch active learning algorithm.

Batch Active learning by Diverse Gradient Embeddings (BADGE) An implementation of the BADGE batch active learning algorithm. Details are provided in o

125 Dec 24, 2022
AI that generate music

PianoGPT ai that generate music try it here https://share.streamlit.io/annasajkh/pianogpt/main/main.py or here https://huggingface.co/spaces/Annas/Pia

Annas 28 Nov 27, 2022
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

TensorFlow Examples This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and so

Aymeric Damien 42.5k Jan 08, 2023
Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track (SIGIR 2021 Full Paper).

Optimizing Dense Retrieval Model Training with Hard Negatives Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma 🔥 News 2021-10

Jingtao Zhan 99 Dec 27, 2022
Equivariant CNNs for the sphere and SO(3) implemented in PyTorch

Equivariant CNNs for the sphere and SO(3) implemented in PyTorch

Jonas Köhler 893 Dec 28, 2022
Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.

Image Segmentation Keras : Implementation of Segnet, FCN, UNet, PSPNet and other models in Keras. Implementation of various Deep Image Segmentation mo

Divam Gupta 2.6k Jan 05, 2023
Testability-Aware Low Power Controller Design with Evolutionary Learning, ITC2021

Testability-Aware Low Power Controller Design with Evolutionary Learning This repo contains the source code of Testability-Aware Low Power Controller

Lee Man 1 Dec 26, 2021
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth, in ICCV 2021 (oral)

RINDNet RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth Mengyang Pu, Yaping Huang, Qingji Guan and Haibin Lin

Mengyang Pu 75 Dec 15, 2022
Robust fine-tuning of zero-shot models

Robust fine-tuning of zero-shot models This repository contains code for the paper Robust fine-tuning of zero-shot models by Mitchell Wortsman*, Gabri

224 Dec 29, 2022
Semi-supervised Domain Adaptation via Minimax Entropy

Semi-supervised Domain Adaptation via Minimax Entropy (ICCV 2019) Install pip install -r requirements.txt The code is written for Pytorch 0.4.0, but s

Vision and Learning Group 243 Jan 09, 2023
[CIKM 2021] Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning

Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning. This repo contains the PyTorch code and implementation for the paper E

Akuchi 18 Dec 22, 2022
Proposed n-stage Latent Dirichlet Allocation method - A Novel Approach for LDA

n-stage Latent Dirichlet Allocation (n-LDA) Proposed n-LDA & A Novel Approach for classical LDA Latent Dirichlet Allocation (LDA) is a generative prob

Anıl Güven 4 Mar 07, 2022
Attention mechanism with MNIST dataset

[TensorFlow] Attention mechanism with MNIST dataset Usage $ python run.py Result Training Loss graph. Test Each figure shows input digit, attention ma

YeongHyeon Park 12 Jun 10, 2022