MBaranPeker

mbaran's starred repositories

3D-Point-Cloud-Curve-Extraction-and-Optimization

This code is designed to extract a representative curve (or a thin center line) from a noisy 3D point cloud, especially when the point cloud has discernible geometric patterns. Given a noisy point cloud that has an inherent structure or shape, the goal is to identify and trace a continuous curve that best captures the essence of that shape.

Language:PythonMIT600

LLaVA

[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.

Language:PythonApache-2.01850900

IR-DRL

A training and testing platform for Industrial Robot applications.

Language:Python3200

BundleSDF

[CVPR 2023] BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects

Language:PythonNOASSERTION97200

Online-3D-BPP-DRL

This repository contains the implementation of paper Online 3D Bin Packing with Constrained Deep Reinforcement Learning.

Language:Python28100

models

A collection of pre-trained, state-of-the-art models in the ONNX format

Language:Jupyter NotebookApache-2.0756800

adaptive_clustering

[ROS package] Lightweight and Accurate Point Cloud Clustering

Language:C++BSD-3-Clause36100

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Language:Jupyter NotebookApache-2.04600900

Pursuit-Evasion-Game-with-Deep-Reinforcement-Learning-in-an-environment-with-an-obstacle

In this study, a multi agent chase-escape problem using Deep Q learning. Actors of the problem are smart evader and smart pursuers with opposite goals. At the beginning of the game these agents have homogeneous properties and evader and pursuits don’t have knowledge about the map. The purpose of the pursuer robots is the catching the evader as fast as it could and the purpose of the evader robot is the escaping as much as it could. Such as this game, where a player's gain is in balance with the loss of other players are called zero-sum games. The end condition, which may differ according to the approach applied, in our study is that “any of the pursuers or evader within the same or neighbor pixel with obstacle or map border” or “one pursuer and evader within the same or neighbor pixel”, in other words, Evader catches by the any of the pursuers or evader hits an obstacle or any pursuers hits an obstacle. A new episode of the game resumes after each collision or cath. In this respect, escape-chase problems are also included in the repeat games class. In this study, the question is what any pursuer or evader can do to improve its performance in a repetitive part of the game is questioned. The method used for this study is Deep Reinforcement Learning. Agents receive rewards or penalties based on their moves within a section and update this information into the Neural Network.

Language:Python1700