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Running Pepper's starred repositories

deepfuturegaze_gan

Deep Future Gaze: Gaze Anticipation on Egocentric Videos Using Adversarial Networks

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xtuner

An efficient, flexible and full-featured toolkit for fine-tuning LLM (InternLM2, Llama3, Phi3, Qwen, Mistral, ...)

Language:PythonApache-2.0359500

fusion_pointclouds

fusion_pointclouds 主要目的为Ubuntu环境下无人车多激光雷达标定之后，将多个激光雷达点云话题/坐标系通过PCL (Point Cloud Library)融合为一个ros点云话题，以便于后期点云地面分割与地面处理等等。

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spinningup

An educational resource to help anyone learn deep reinforcement learning.

Language:PythonMIT990000

image-matching-webui

🤗 image matching toolbox webui

Language:PythonApache-2.064900

STA-VPR

This repository contains code for our RA-L & ICRA2021 paper "STA-VPR: Spatio-temporal Alignment for Visual Place Recognition" by Feng Lu, Baifan Chen, Xiang-Dong Zhou, and Dezhen Song.

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CricaVPR

Official repository for the CVPR 2024 paper "CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition".

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SelaVPR

Official repository for the ICLR 2024 paper "Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition".

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VDO_SLAM

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

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orbslam2_learn

Learn ORBSLAM2 and divide the source code into many parts according to their function which can be easily built by the learner from my blog.

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CosPlace

Official code for CVPR 2022 paper "Rethinking Visual Geo-localization for Large-Scale Applications"

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deep-visual-geo-localization-benchmark

Official code for CVPR 2022 (Oral) paper "Deep Visual Geo-localization Benchmark"

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cnnimageretrieval-pytorch

CNN Image Retrieval in PyTorch: Training and evaluating CNNs for Image Retrieval in PyTorch

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HAF

This repo is the PyTorch implementation of ICASSP2021 paper "HIERARCHICAL ATTENTION FUSION FOR GEO-LOCALIZATION"

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netvlad

NetVLAD: CNN architecture for weakly supervised place recognition

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OpenIBL

[ECCV-2020 (spotlight)] Self-supervising Fine-grained Region Similarities for Large-scale Image Localization. 🌏 PyTorch open-source toolbox for image-based localization (place recognition).

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yolov4-tiny-pytorch

这是一个YoloV4-tiny-pytorch的源码，可以用于训练自己的模型。

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A general framework for map-based visual localization. It contains 1) Map Generation which support traditional features or deeplearning features. 2) Hierarchical-Localizationvisual in visual(points or line) map. 3)Fusion framework with IMU, wheel odom and GPS sensors.

MIT72000

VPR-Bench

This repository contains the codebase and all the relevant information for our IJCV paper on VPR-Bench: An open-source Visual Place Recognition Evaluation Framework

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Hierarchical-Localization

Visual localization made easy with hloc

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visloc-relapose

A 2D Visual Localization Framework based on Essential Matrices [ICRA2020]

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CVPR2024-Paper-Code-Interpretation

cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集，极市团队整理

1238600

Self_Driving_Car

This Project gives a glimpse of a real-world self-driving car.This is a model totally based on Nvidia Self Driving Car model.A Convolutional neural network(CNN) is trained to map raw pixels from a single front-facing camera directly to steering commands.

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cyber_meld

Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios

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Self-Driving-Car-Path-Planning-Project

Navigate to goal with High Definition(HD) Map, Sensor Fusion, Finite State Machine and Optimise by Cost Function.

Language:C++MIT700

Object-Recognition-for-Autonomous-Driving-System-MATLAB-project

The goal of this project is to provide object detection and information on environment model on traffic activity which helps autonomous vehicles or surveillance systems. Computer vision is an essential component for autonomous scars. Accurate detection of vehicles, street buildings, pedestrians, and road signs could assist self-driving cars the drive as safely as humans. However, object detection has been a challenging task for years since images of objects in the real-world environment are affected by illumination, rotation, scale, and occlusion. A unified object detection model, You Only Look Once (YOLO), is used which could directly regress from the input image to object class scores and positions. In this project, we applied YOLO to two different datasets to test its general applicability. We fully analyzed its performance from various aspects on KITTI data set which is specialized for autonomous driving. We proposed a novel technique called memory map, which considers inter-frame information, to strengthen YOLO's detection ability in the driving scene. We broadened the model's applicability scope by applying it to a new orientation estimation task. For this project objective is to provide a information of quality and environmental model on traffic activity and to signal potentially anomalous situation and also apply various machine learning models for object detection such as SVM and CNN and compare and contrast the results with YOLO .

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