This repo is implementation for PointNet and PointNet++ for binary (0 or 1) in pytorch.

Prediction of vegetation coverage maps from High Density Lidar data, in a weakly supervised deep learning setting.

This is a repository of the code used in the "Midline-Constrained Loss in the Anatomical Landmark Segmentation of 3D Liver Models" paper from MIUA 2025

Code for "An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment", Rice et al. 2025

Interactive PointNet++ workshop for point cloud deep learning. Train a deep learning model to classify 3D shapes, visualize features. GPU-optimized Jupyter notebook.

The project of Neuron Tracking

Frustum Pointnet Implementation on KITTI and Lyft Dataset

GSNet implementation ported to a more modern stack, with additional options for backbone feature extractors and stability-aware reweighting from AnyGrasp.

MSc thesis work conducted @ MBZUAI. The work was presented and published at VISAPP 2023 conference.

3D bounding box prediction using Point Cloud and RGB image | Models: Transformer, Multimodal(PointNet++, ResNet), PointNet++

Applying RandAugment on PointNet++

✨ PointNet++ feature extractor and output heads implemented in TensorFlow 1.15 with Keras Models

Code and Data for the paper "LPF-Defense: 3D Adversarial Defense based on Frequency Analysis", PLoS ONE

Semantic segmentation of LIDAR point clouds from the KITTI-360 dataset using a modified PointNet2. This is a Python and PyTorch based implementation using Jupyter Notebooks.

Pytorch implementation of PointNeXt

Implementation of PointNet

A pytorch implementation of PointNet and PointNet++

Official implementation of the paper "Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space"

Efficient Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction