A visualisation engine for particle simulations
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Updated
Jul 2, 2024 - Python
A visualisation engine for particle simulations
Efficient and parallel algorithms for point cloud registration [C++, Python]
An easy-to-use and robust library for seamless interaction with OAK cameras and the DepthAI API. It aims to bridge the gap between the DepthAI API and SDK, allowing for built-in integration with OpenCV and Open3D "out-of-the-box."
Pointcloud-based graph SLAM written in C++ using open3D library.
This repository contains the code developed for my MSc project titled "Exploring Photogrammetry for High-Fidelity 3D Model Generation: A Power Grid Inspection Study", as well as some of the results. The thesis was written in collaboration with SINTEF, and the degree issued by DTU. The full thesis can be downloaded from the below link.
Lidar Obstacle Detection using RANSAC and DBSCAN
「詳解 3次元点群処理 Pythonによる基礎アルゴリズムの実装」実行用Dockerコンテナ
Three workflows based on aerial imagery or LiDAR point clouds to evaluate the surface available on rooftops by detecting objects.
Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)
My personal guide to the great Python library Open3D.
A research-purposed, GUI-powered, Python-based framework that allows easy development of dynamic point-cloud (and accompanying image) data processing pipelines.
Point cloud map evaluation library for the FusionPortable dataset. Metrics include Mean Map Entropy (MME), RMSE, Accuracy(mean error), Precision(standard deviation), completeness(overlap ratio), chamfer distance and F1-score at all levels of 1/2/5/10/20cm.
Addon to import different photogrammetry formats into Blender
Use the power of the Ope3d library with the visual scripting environment of Sverchok inside Blender
The aim of my project was to develop a user-friendly method for generating realistic 3D models and displaying them on smartphones using augmented reality. The only input required from the user is a short video of the desired object, which can be uploaded to the app. This project uses a modified version of Nvidia's Instant-NeRF.
Using PyTorch's MiDaS model and Open3D's point cloud to map a scene in 3D 🏞️🔭
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