957 Open source projects that are alternatives of or similar to Pointnet Keras

Grid-GCN for Fast and Scalable Point Cloud Learning

A PyTorch implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)

PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling （CVPR 2020）

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network

Papers and Datasets about Point Cloud.

PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

3D point cloud datasets in HDF5 format, containing uniformly sampled 2048 points per shape.

Tensorflow implementation : U-net and FCN with global convolution

3D Graph Neural Networks for RGBD Semantic Segmentation

Collection of computer vision models, ready to be included in a JVM project

A clean PointNet++ segmentation model implementation. Support batch of samples with different number of points.

Superpixel-based semantic segmentation, with object pose estimation and tracking. Provided as a ROS package.

Image Test Time Augmentation with PyTorch!

A lean C++ library for working with point cloud data

PyTorch Implementation of Stacked U-Nets (SUNets)

Pytorch implementation of 'Graph Attention Convolution for Point Cloud Segmentation'

Crop/Weed Field Image Dataset

TF2 implementation of PointNet for segmenting point clouds

PaddlePaddle End-to-End Development Toolkit（『飞桨』深度学习全流程开发工具）

deep learning for image processing including classification and object-detection etc.

基于Tensorflow2开发的优秀扩展包及项目

Test Time Augmentation (TTA) wrapper for computer vision tasks: segmentation, classification, super-resolution, ... etc.

Pytorch framework for doing deep learning on point clouds.

An open-source semi-automated processing chain for urban OBIA classification.

ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics

PAPC is a deep learning for point clouds platform based on pure PaddlePaddle

OpenMMLab Model Compression Toolbox and Benchmark.

All-in-one Toolbox for Computer Vision Research.

Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

Resurces for MRI images processing and deep learning in 3D

Caffe models (including classification, detection and segmentation) and deploy files for famouse networks

天池医疗AI大赛[第一季]：肺部结节智能诊断 UNet/VGG/Inception/ResNet/DenseNet

Collections of model quantization algorithms

A Python toolbox for computer vision research and project

All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN

🚕 Fast and robust clustering of point clouds generated with a Velodyne sensor.

Generalized Data-free Universal Adversarial Perturbations

Sandbox for training deep learning networks

A python tool for fitting primitives 3D shapes in point clouds using RANSAC algorithm

This is an official implementation of our NeurIPS 2021 paper "HRFormer: High-Resolution Transformer for Dense Prediction".

Classification and Segmentation with Mask-RCNN of Skin Cancer using ISIC dataset

Semantic3D segmentation with Open3D and PointNet++

Pytorch framework for doing deep learning on point clouds.

《深度学习与计算机视觉》配套代码

A place for the development version of the changepoint package on CRAN.

Accompanying library for the Record3D iOS app (https://record3d.app/). Allows you to receive RGBD stream from iOS devices with TrueDepth camera(s).

Tensorflow Implementation of Pixel Transposed Convolutional Networks (PixelTCN and PixelTCL)

A pytorch implementation of BERT-based relation classification

KiTS19——2019 Kidney Tumor Segmentation Challenge

BGP Hijack Detection

Gaussian Process package based on data augmentation, sparsity and natural gradients

Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo

3D Heritage Online Presenter

OneNote (desktop) add-in to manage OneNote pages by page tags

The code includes all the file that you need in the training stage for FCN

Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.

Julia package of loss functions for machine learning.