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A high-performance C++ headers for real-time object detection and segmentation using YOLO models, leveraging ONNX Runtime and OpenCV for seamless integration. Supports multiple YOLO (v5, v7, v8, v9…
open Multiple View Geometry library. Basis for 3D computer vision and Structure from Motion.
Community for applying LLMs to robotics and a robot simulator with ChatGPT integration
A list of awesome neural symbolic papers.
Running large language models on a single GPU for throughput-oriented scenarios.
Reading list for research topics in multimodal machine learning
A collaboration friendly studio for NeRFs
A Unified Framework for Surface Reconstruction
pySLAM is a visual SLAM pipeline in Python for monocular, stereo and RGBD cameras. It supports many modern local and global features, different loop-closing methods, a volumetric reconstruction pip…
Python implementation of Visual Odometry algorithms from http://rpg.ifi.uzh.ch/
Calculates the extrinsic calibration between a Navtech radar and a 3D (Velodyne) lidar
Convert KITTI dataset to ROS bag file the easy way!
Target-free Extrinsic Calibration of a 3D Lidar and an IMU
Bayesian statistics graduate course
Provide mapping and localization pipelines based on kapture format
A Code Release for Mip-NeRF 360, Ref-NeRF, and RawNeRF
Create Dense Depth Map Image for Known Poisitioned Camera from Lidar Point Cloud
Preparing for machine learning interviews
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
Unsupervised radar odometry combining deep learning with classical state estimation
torch version of instant-ngp, image rendering
A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book
Uncertainty Quantification in Deep Learning
Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
A pattern-based approach for learning technical interview questions
Tutorial for working with the KITTI odometry dataset in Python with OpenCV. Includes a review of Computer Vision fundamentals.