🔥3D点云目标检测&语义分割(深度学习)-SOTA方法,代码,论文,数据集等

ROS & ROS2 Implementation of Patchwork++

A fast and memory-efficient libarary for sparse transformer with varying token numbers (e.g., 3D point cloud).

3D点云语义分割汇总，所有顶会论文以及一些arxiv上的最新论文

A C++ version for "A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles" 2018 ITSC

This is the official repository of the original Point Transformer architecture.

Fast Segmentation of 3D Point Clouds A Paradigm on LiDAR Data for Autonomous Vehicle Applications

Geodesic-Former: a Geodesic-Guided Few-shot 3D Point Cloud Instance Segmenter (ECCV 2022)

The four major frameworks for 3D point cloud sparse acceleration, which are currently mainstream, are compared. These include MIT-HAN-LAB's torchsparse, NVIDIA's MinkowskiEngine, TuSimple's spconv, and Facebook Research's SparseConvNet.

Extended Kalman Filter and Deep Learning to detect vehicles from RGB and LiDAR data (Sensor Fusion and Tracking project of the Udacity Self-Driving Car Engineer Nanodegree Program)

[ICRA 2024] Official Implementation of the Paper "Parameter-efficient Prompt Learning for 3D Point Cloud Understanding"

This is the implementation of Recycle Maxpooling Module for Point Cloud Analysis

The official implementation code of Paper "PointCVaR: Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification" in AAAI 2024 (Oral)

Paper on 3D Point Cloud Processing

A tutorial for learning the knowledge and techniques about 3D point clouds.

A PyTorch implementation of Point Transformer that can handle the input data in batch mode.

Official code for the NeurIPS 2024 paper "Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need"

3D point cloud data (npy file) plot(viewer) in python and mayavi.

3D Scene Reconstruction Based on Stereo Vision.

Course submission material for Sensor Fusion and Camera based tracking using Extended Kalman Filters for Udacity Self Driving Nanodegree.