#
chaytonmin
Follow
Starred repositories
An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.
A simple LiDAR odometry method reducing the complexity and configuration burden of localization algorithms.
A simple and easy-to-use learning-free MOS approach for point cloud data.
A very simple GRPO implement for reproducing r1-like LLM thinking.
This is a ROS package for lidar odometry implementation using rotation optimization method.
LimSim & LimSim++: Integrated traffic and autonomous driving simulators with (M)LLM support
Converting bin files scanned by LIDAR of autonomous vehicles into pcd files for further processing
Implementation of the new SOTA for model based RL, from the paper "Improving Transformer World Models for Data-Efficient RL", in Pytorch
Awesome papers about Autonomous Ground Robot System in Unstructured Outdoor Environments
Fully open reproduction of DeepSeek-R1
Awesome lists about framework figures in papers
Video Depth Anything: Consistent Depth Estimation for Super-Long Videos
[CVPR 2024] Official RT-DETR (RTDETR paddle pytorch), Real-Time DEtection TRansformer, DETRs Beat YOLOs on Real-time Object Detection. 🔥 🔥 🔥
Official implementation of the paper "HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting""
Official implementation of the paper "HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving"
3D Gaussian Rendering PlayGround: an open-source autonomous driving closed-loop simulator demo using 3D Gaussian Splatting tech
An open source code repository of driving world models, with training, inferencing, evaluation tools, and pretrained checkpoints.
TrafficBots V1.5: TrafficBots + HPTR. 3rd place solution for Waymo Open Sim Agent Challenge 2024.
A generative world for general-purpose robotics & embodied AI learning.
Official implementation of Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
[ICLR2025] GenPercept: Diffusion Models Trained with Large Data Are Transferable Visual Models