957 Open source projects that are alternatives of or similar to Dgcnn.pytorch

Papers and Datasets about Point Cloud.

Grid-GCN for Fast and Scalable Point Cloud Learning

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

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

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

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

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

Keras implementation for Pointnet

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

3D Graph Neural Networks for RGBD Semantic Segmentation

PyTorch Implementation of Stacked U-Nets (SUNets)

Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

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

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

《深度学习与计算机视觉》配套代码

TF2 implementation of PointNet for segmenting point clouds

基于Tensorflow2开发的优秀扩展包及项目

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

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

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

Pytorch framework for doing deep learning on point clouds.

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

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

All-in-one Toolbox for Computer Vision Research.

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

Semantic3D segmentation with Open3D and PointNet++

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

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

Resurces for MRI images processing and deep learning in 3D

Image Test Time Augmentation with PyTorch!

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

Generalized Data-free Universal Adversarial Perturbations

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

Crop/Weed Field Image Dataset

Sandbox for training deep learning networks

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

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

A Python toolbox for computer vision research and project

OpenMMLab Model Compression Toolbox and Benchmark.

Tensorflow implementation : U-net and FCN with global convolution

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

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

Pytorch framework for doing deep learning on point clouds.

Collections of model quantization algorithms

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

High-quality Neural Networks for Computer Vision 😎

Implementation of EfficientNet model. Keras and TensorFlow Keras.

Morfessor is a tool for unsupervised and semi-supervised morphological segmentation

Deep neural network to extract intelligent information from invoice documents.

Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. ICIAR 2018 Grand Challenge on BreAst Cancer Histology images (BACH)

EchoNet-Dynamic is a deep learning model for assessing cardiac function in echocardiogram videos.

Implement some models of RGB/RGBD semantic segmentation in PyTorch, easy to run. Such as FCN, RefineNet, PSPNet, RDFNet, 3DGNN, PointNet, DeepLab V3, DeepLab V3 plus, DenseASPP, FastFCN

Segmentation realize Deeperlab only segmentation part

Official Pytorch Code of KiU-Net for Image Segmentation - MICCAI 2020 (Oral)

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

The code for paper "CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning"

Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds

This repository consists of all my Machine Learning Projects.

A native Tensorflow implementation of semantic segmentation according to Multi-Scale Context Aggregation by Dilated Convolutions (2016). Optionally uses the pretrained weights by the authors.