{"id":19255579,"url":"https://github.com/pauldanielml/point-cloud-shape-approximation","last_synced_at":"2026-05-18T04:40:48.621Z","repository":{"id":176743668,"uuid":"274217237","full_name":"PaulDanielML/Point-Cloud-Shape-Approximation","owner":"PaulDanielML","description":"Using k-order Markov Path Optimization to fit shapes to sparse point cloud 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Approximating shapes, positions and sizes from point cloud data for robotic grasping\n\nAuthor: Paul Daniel (pdd@mp.aau.dk)\n\nApproximation process\t           |  Console output\n:---------------------------------:|:-------------------------:\n![](/media/gif_1.gif \"Shape approximation\")  |  ![](/media/img_1.png \"Output\")\n\nYouTube Video:\n\n[![video](https://img.youtube.com/vi/LC4D6ko-C4c/0.jpg)](https://www.youtube.com/watch?v=LC4D6ko-C4c)  \n\n\nThe following pseudocode shows the structure of the created algorithm used for finding the approximations. \n\n![](/media/algo.png \"Algorithm\")\n\n\nFinal project for the TU Berlin course \"AI \u0026 Robotics: Lab Course\". \nTo reproduce set up rai as described [here](https://github.com/MarcToussaint/robotics-course#setup-for-robotics-practical-in-simulation). \nAfter successful setup, switch to the folder *course3-Simulation*:\n\n    cd robotics-course/course3-Simulation\n\nclone the this repo there: \n\n    git clone 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