smitdumore

smitdumore's Stars

• yassouali/pytorch-segmentation

  :art: Semantic segmentation models, datasets and losses implemented in PyTorch.

  Language:Jupyter Notebook1.6k374

• MjdMahasneh/Simple-PyTorch-Semantic-Segmentation-CNNs

  PyTorch Implementation of Semantic Segmentation CNNs: This repository features key architectures like UNet, DeepLabv3+, SegNet, FCN, and PSPNet. It's crafted to provide a solid foundation for Semantic Segmentation tasks using PyTorch.

  Language:Python7

• milesial/Pytorch-UNet

  PyTorch implementation of the U-Net for image semantic segmentation with high quality images

  Language:Python8.7k2.4k

• dheera/ros-semantic-segmentation

  ROS package for semantic segmentation

  Language:Python10422

• floatlazer/semantic_slam

  Real time semantic slam in ROS with a hand held RGB-D camera

  Language:C++612177

• snshen/Learning-for-3D-Vision

  Assignments from 16-825 Learning for 3D Vision at Carnegie Mellon University

  Language:Python51

• Yacynte/Monocular-Visual-Odometry

  visual (inertia) odometry of a drone with a monocular camera

  Language:Python3

• ZJU-FAST-Lab/VID-Dataset

  Language:C++5514

• ZJU-FAST-Lab/VID-Fusion

  VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation

  Language:C++11425

• harish-vnkt/structure-from-motion

  A simplified version of incremental structure from motion to reconstruct the 3D structure of a scene from a sequence of 2D images

  Language:Python7419

• iamjadhav/autopano

  CMSC 733 Project 1 - AutoPano: Repository for image stitching using traditional CV and deep learning approach

  Language:Python31

• Yashas120/Multiview-3D-Reconstruction

  Using traditional image processing techniques to construct 3D point cloud of objects. Incremental Structure from Motion (SfM) is used, a popular SfM algorithm for 3D reconstruction for reconstruction. The method is then evaluated using certain 3D reconstruction datasets.

  Language:Python7321

• himadrir/posenet

  Learning based pose-regressor for visual-odometry-frontend

  Language:Jupyter Notebook2

• naitri/SFM

  Structure From Motion : A python implementation to reconstruct a 3D scene and obtain camera poses with respect to scene

  Language:Python72

• imkaywu/open3DCV

  open source 3D computer vision library

  Language:C++6916

• dtcMLOps/sfM_danPipeline

  A simple structure from motion pipeline in c++ using OpenCV and PCL

  Language:C++288

• StevenBlack/hosts

  🔒 Consolidating and extending hosts files from several well-curated sources. Optionally pick extensions for porn, social media, and other categories.

  Language:Python26.1k2.2k

• concept-graphs/concept-graphs

  Official code release for ConceptGraphs

  Language:Python31048

• 1989Ryan/Semantic_SLAM

  Semantic SLAM using ROS, ORB SLAM, PSPNet101

  Language:C++28751

• kdurgaprakash/Motion-Planning-using-Reinforcement-Learning

  Motion Planning of autonomous vehicle using Proximal Policy Optimization

  Language:Jupyter Notebook22

• zeidk/enpm809Y_fall2023

  Source files for C++ and ROS packages

  54

• itej89/AnytimeDynamic_Astar_planner

  This repository contains Anytime Dynamic A* algorithm implementaiton for path planning of a given graph

  Language:Python1

• dpiet/cpp-boilerplate

  Simple cpp boilerplate with cmake and gtest

  Language:CMake356

• AmeyDeshpande97/Motion-Prediction-of-Agents-in-the-Vicinity-of-Self-Driving-Car

  The goal of the project was to predict the motion of an autonomous vehicle and the surrounding agents given their trajectory for the past one second. For this, we used rasterized images as an input to a CNN baseline. To the given parameters, we added the velocities along x and y direction. This helped predicting instantaneous velocity of the agent at every time step. In addition, the baseline was modified by adding an LSTM decoder to study their impact on predictions. Instantaneous velocity was an added parameter for the model and predicting instantaneous velocities can be used to improve the motion prediction of the agents and can facilitate agent interaction and cooperative driving

  Language:Python6

• agrimgupta92/sgan

  Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018

  Language:Python807259

• applied-ai-collective/Pacman-Deep-Q-Network

  Deep Reinforcement Learning Pacman in Pytorch

  Language:Python74

• m5823779/motion-planner-reinforcement-learning

  End to end motion planner using Deep Deterministic Policy Gradient (DDPG) in gazebo

  Language:Python19546

• tychovdo/PacmanDQN

  Deep Reinforcement Learning in Pac-man

  Language:Python272119

• niconielsen32/VisualSLAM

  Language:Python5721

• backbencher00/stockprice_prediction_LSTM_GRU

  stock price prediction by hybride model of LSTM and GRU

  Language:Jupyter Notebook41