赵伟松's starred repositories

UTM

Virtual machines for iOS and macOS

Language:SwiftLicense:Apache-2.0Stargazers:25613Issues:356Issues:2901

PythonRobotics

Python sample codes for robotics algorithms.

Language:PythonLicense:NOASSERTIONStargazers:22390Issues:507Issues:349

Grounded-Segment-Anything

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

Language:Jupyter NotebookLicense:Apache-2.0Stargazers:14307Issues:115Issues:375

clip-as-service

🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP

Language:PythonLicense:NOASSERTIONStargazers:12309Issues:221Issues:606

open_clip

An open source implementation of CLIP.

Language:PythonLicense:NOASSERTIONStargazers:9335Issues:76Issues:454

LAVIS

LAVIS - A One-stop Library for Language-Vision Intelligence

Language:Jupyter NotebookLicense:BSD-3-ClauseStargazers:9298Issues:97Issues:632

clip-retrieval

Easily compute clip embeddings and build a clip retrieval system with them

Language:Jupyter NotebookLicense:MITStargazers:2287Issues:23Issues:224

habitat-lab

A modular high-level library to train embodied AI agents across a variety of tasks and environments.

Language:PythonLicense:MITStargazers:1842Issues:47Issues:659

Anything-3D

Segment-Anything + 3D. Let's lift anything to 3D.

Language:PythonLicense:MITStargazers:1522Issues:35Issues:15

Pointcept

Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23)

Language:PythonLicense:MITStargazers:1381Issues:20Issues:271

cube_slam

CubeSLAM: Monocular 3D Object Detection and SLAM

Language:C++License:NOASSERTIONStargazers:828Issues:35Issues:61

DynaSLAM

DynaSLAM is a SLAM system robust in dynamic environments for monocular, stereo and RGB-D setups

Language:C++License:NOASSERTIONStargazers:797Issues:22Issues:98

SegmentAnyRGBD

Segment Any RGBD

Language:PythonLicense:NOASSERTIONStargazers:754Issues:12Issues:11

VDO_SLAM

VDO-SLAM: A Visual Dynamic Object-aware SLAM System

Language:C++License:NOASSERTIONStargazers:718Issues:19Issues:56

PointTransformerV3

[CVPR'24 Oral] Official repository of Point Transformer V3 (PTv3)

Language:PythonLicense:MITStargazers:643Issues:17Issues:77

openscene

[CVPR'23] OpenScene: 3D Scene Understanding with Open Vocabularies

Language:PythonLicense:Apache-2.0Stargazers:594Issues:19Issues:85

Awesome_Dynamic_SLAM

Dynamic SLAM, Life-long SLAM Research(Lidar, Visual, Sensor Fusion etc.)

oneformer3d

[CVPR2024] OneFormer3D: One Transformer for Unified Point Cloud Segmentation

Language:PythonLicense:NOASSERTIONStargazers:269Issues:8Issues:64

refusion

ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals

Language:C++License:NOASSERTIONStargazers:257Issues:19Issues:14

RDS-SLAM

DS-SLAM: Real-Time Dynamic SLAM Using Semantic Segmentation Methods

Language:Jupyter NotebookLicense:GPL-3.0Stargazers:218Issues:7Issues:27

YOLO_ORB_SLAM3

This is an improved version of ORB-SLAM3 that adds an object detection module implemented with YOLOv5 to achieve SLAM in dynamic environments.

Language:C++License:NOASSERTIONStargazers:187Issues:2Issues:10

SG-SLAM

SG-SLAM: A Real-Time RGB-D Visual SLAM toward Dynamic Scenes with Semantic and Geometric Information

Language:C++License:GPL-3.0Stargazers:148Issues:4Issues:32

Omni-PQ

[ICRA 2023] From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds

Face-expression-and-ethnic-recognition

Two image models for face expression recognition and for ethnic classification

PinSout

Accelerating 3D Indoor Space Construction from Point Clouds with Deep Learning

mid-fusion

Code for ICRA 2019 work "MID-Fusion Octree-based Object-Level Multi-Instance Dynamic SLAM"

Language:C++License:BSD-3-ClauseStargazers:48Issues:2Issues:1

Face_Recognition_Project

Gender/Race/Emotion classifications based on facial multi-attribute detection were realized through data pre-processing, face detection and extraction by OpenCV, developing training models through “Fisherfaces” and “Convolutional Neural Network” approaches by TensorFlow and Keras with accuracy of 98.44%, 84.24%, and 70% respectively. A web app was developed for better demonstration and further model optimization by expanding training dataset and user labeling, adopted Nginx to serve web server and deployed on DigitalOcean.

Language:PythonStargazers:11Issues:1Issues:0