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The official Python client for the Huggingface Hub.
Official implementation for HybridDepth Model (WACV 2025, ISMAR 2024)
[CVPR 2024] Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Foundation Model for Monocular Depth Estimation
Code for "Interpolation-Prediction Networks for Irregularly Sampled Time Series", ICLR 2019.
A Robust and Versatile Monocular Visual-Inertial State Estimator
Monocular Visual Odometry is a technique used to estimate the pose of camera mounted on a vehicle, say a car which helps in creating localization map for an autonomous self driving vehicle
Visual SLAM/odometry package based on NVIDIA-accelerated cuVSLAM
Pangolin is a lightweight portable rapid development library for managing OpenGL display / interaction and abstracting video input.
Yahboom Jetbot AI robot with HD camera coding with Python compatible with 4GB(A02/B01)
Time Series Analysis with Python Cookbook, published by Packt
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
A ROS2 Humble package natively implementing ORB-SLAM3 V1.0 VSLAM framework
Code for predicting change in qSOFA using features extracted from physiological signals and/or electronic health record data.
Code repository for deep clustering analysis of ICU data
Missing data imputation for longitudinal multi-variable EHR data. Paper in JAMIA.
I introduce the basic idea and implementation of 5 imputation approaches. In short, filling with a single value works well for a shorter period of missing values. MICE should be one of your first c…
Kernel similarity for classification and clustering of multi-variate time series with missing values.
Time-Series Clustering: Overview, R-packages
AC_TPC: Temporal Phenotyping using Deep Predicting Clustering of Disease Progression
Code repository for paper "Development and clinical utility of machine learning algorithms for dynamic longitudinal real-time estimation of progression risks in active surveillance of early prostat…
Code exercises for the SLAM course in 'Computer Vision, LiDAR processing, and Sensor Fusion for Autonomous Driving' lecture series