UArizona DataLab Workshops
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Updated
Jun 12, 2024 - Jupyter Notebook
UArizona DataLab Workshops
LiDAR processing ROS2. Segmentation algorithm: "Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process". Clustering algorithm: "Curved-Voxel Clustering for Accurate Segmentation of 3D LiDAR Point Clouds with Real-Time Performance".
[CVPR 2024 Oral - Best paper award candidate] Official repository of "PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness"
Tutorial for creating DSM from LiDAR data in R
A fully templated C++ implementation of general-use algorithms for robotic perception and visual servoing.
A pipeline for semantic segmentation, densification, and planar flattening for improving voxelization and mesh reconstruction quality of airborne LiDAR data.
C++/Python Sparse Volumetric TSDF Fusion
Compute point cloud geometric features from python
A Modular Optimization framework for Localization and mApping (MOLA)
Automatic Processing of Terrestrial-Based Technologies Point Cloud Data for Forestry Purposes
Semantic Segmentation of LiDAR Point Cloud for Autonomous Vehicles. Implementation using RangeNet architecture as baseline model.
Fast and robust global registration for terrestrial robots @ ICRA2022
From https://github.com/kneehit/LIDAR-Obstacle-Detection, ROS version
DTU course 34761 Robot Autonomy, Spring 2024
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."
Python SDK for accessing TCP API
Change detection between two LiDAR point clouds using voxelization. Highlight discrepancies in geometry and classification.
A Lightweight Encoder-Decoder Network for LiDAR-based Road- Object Semantic Segmentation
[RAL] Keypoint Matching for Point Cloud Registration Using Multiplex Dynamic Graph Attention Networks
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