zhangguanghui1

awesome-point-cloud-analysis

A list of papers and datasets about point cloud analysis (processing)

cvpr2019

cvpr2019 papers，极市团队整理

D3Feat

Implementation of CVPR'20 oral paper - D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features

D3Feat.pytorch

[PyTorch] Implementation of CVPR'20 oral paper - D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features https://arxiv.org/abs/2003.03164

DeepGlobalRegistration

[CVPR 2020 Oral] A differentiable framework for 3D registration

GraphLineMatching

The code for Robust Line Segments Matching via Graph Convolution Networks

Grid-GCN

Grid-GCN for Fast and Scalable Point Cloud Learning

jsis3d

Joint Semantic-Instance Segmentation of 3D Point Clouds

learning_to_sample

A learned sampling approach for point clouds (CVPR 2019)

LightConvPoint

MANet

Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)

MVision

机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶

oh-my-python

剑指offer（python版）/ 算法图解 / python基础 / 数据结构

OpenSfM

Open source Structure from Motion pipeline

PCDet

PCDet Toolbox in PyTorch for 3D Object Detection from Point Cloud

pcrnet_pytorch

This is a pytorch implementation of PCRNet

PF-Net-Point-Fractal-Network

CVPR2020 PF-Net: Point Fractal Network for 3D Point Cloud Completion

PointGroup

PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation

powerful-gnns

How Powerful are Graph Neural Networks?

pvcnn

[NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning

pytorch-superglue

SuperGlue: Learning Feature Matching with Graph Neural Networks

pytorch_geometric

Geometric Deep Learning Extension Library for PyTorch

RandLA-Net

RandLA-Net in Tensorflow (CVPR 2020, Oral)

SA-SSD

SampleNet

Differentiable Point Cloud Sampling (To appear in CVPR 2020 as an oral presentation)

SPH3D-GCN

Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds

st-gcn

Spatial Temporal Graph Convolutional Networks (ST-GCN) for Skeleton-Based Action Recognition in PyTorch

SuperGluePretrainedNetwork

SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

unsup3d

(CVPR'20 Oral) Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

vision

Datasets, Transforms and Models specific to Computer Vision