🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)

PyTorch implementation of RandLA-Net

Myria3D: Aerial Lidar HD Semantic Segmentation with Deep Learning

:four_leaf_clover: Pytorch Implementation of RandLA-Net (https://arxiv.org/abs/1911.11236)

RandLA-Net's implementation with Pytorch

Pytorch Reimplementation of RandLA-Net for S3DIS and Semantickitti

Python 3.6, Tensorflow 2.6, CUDA 11.4 and cudnn in RTX 3090

RandLA-Net改进版

Our PyTorch implementation of RandLA-Net (https://github.com/QingyongHu/RandLA-Net)

This GitHub repository has been created for the research project titled "Improving Aerial Targeting Precision: A Study on Point Cloud Semantic Segmentation with Advanced Deep Learning Algorithms."

ROS node for RandLA-Net.

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Updated Randla_net on Toronto_3D dataset

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This is the unofficial implementation of RandLA-Net (CVPR2020, Oral presentation) using Mindspore, a simple and efficient neural architecture for semantic segmentation of large-scale 3D point clouds.

Refined RandLa-Net implementation forked from https://github.com/aRI0U/RandLA-Net-pytorch

This is changed RandLA-Net by LYY. It is supported tensorflow2.6 and python3.6. Escipally, this code repository is suiitable for Senmantic3D datasets.

Tree detection from UAV LiDAR point clouds using Local Maxima Filter and RandLA-Net, developed for an Environmental Data Analytics master’s course.

PyTorch Implementation of RandLA-Net

原github网址：https://github.com/liuxuexun/RandLA-Net-Pytorch-New/tree/master

PCAM implementation by using RandLA-Net as backbone

This repository contains the implementation of RandLA-Net (CVPR 2020 Oral) in PyTorch-Lightning

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original code of RandLA-Net with TensorFlow2

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Optimizing the RandLA-Net model using point cloud density estimation

End-to-end pipeline for reconstructing 3D environments from videos, performing object detection with RandLA-Net on S3DIS, and generating point clouds from text/images using OpenAI’s Point-E.