kms-team Goto Github PK

applied-deep-learning

Applied Deep Learning Course

awesome-3d-object-detection-for-autonomous-driving

3D Object Detection for Autonomous Driving: A Comprehensive Survey (IJCV 2023)

awesome-3d-reconstruction-papers

A collection of 3D reconstruction papers in the deep learning era.

awesome-multiple-object-tracking

Resources for Multiple Object Tracking (MOT)

awesome-nerf

A curated list of awesome neural radiance fields papers

awesome-vision-attentions

Summary of related papers on visual attention. Related code will be released based on Jittor gradually.

awesome-vision-language-navigation

b2qt-pi4

bytetrack

[ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box

cvpr2023-papers-with-code

CVPR 2023

droid-slam

gnnpapers

Must-read papers on graph neural networks (GNN)

google-research

Google Research

icra-2022-slam-paper-list

Unofficial ICRA 2022 SLAM paper list

image-matching-paper-list

A personal list of papers and resources of image matching and pose estimation, including perspective images and panoramas.

image-to-latex

junbotvoice

kms-team.github.io

Blog of our Journeys

kms_eye

A simple app visualize for my project about VO/VIO SLAM

kms_iot

Simple IoT app for visual data from sensor via MQTT protocol and MongoDB binding

kms_seedcounter

kmslogger

lightning-nerf

[ICRA 2024] Lightning NeRF: Efficient Hybrid Scene Representation for Autonomous Driving

ltslam

You can learn slam step by step，there are lot of tutorials

medicalsupporter

hmm hmm

ml-papers-explained

Explanation to key concepts in ML

mykms

nerf-slam

NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields. https://arxiv.org/abs/2210.13641 + Sigma-Fusion: Probabilistic Volumetric Fusion for Dense Monocular SLAM https://arxiv.org/abs/2210.01276

papers

ros-llm

ROS-LLM is a framework designed for embodied intelligence applications in ROS. It allows natural language interactions and leverages Large Language Models (LLMs) for decision-making and robot control. With an easy configuration process, this framework allows for swift integration, enabling your robot to operate with it in as little as ten minutes.