Stars
📸 PyTorch implementation of MobileNetV3 for real-time semantic segmentation, with pretrained weights & state-of-the-art performance
SAnE: Smart annotation and evaluation tool for point cloud data
The Kalibr visual-inertial calibration toolbox
Fast Image-Based Geometric Change Detection Given a 3D Model
TRI-ML Monocular Depth Estimation Repository
Repository for the paper "Lidar-Camera Co-Training for Semi-Supervised Road Detection"
This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"
Code used in generating results found in my ICRA2015 paper http://www.zjtaylor.com/welcome/download_pdf?pdf=ICRA2015.pdf&target=_blank
Code for Automatic Multimodal Calibration of a Sensor Array
SemanticKITTI API for visualizing dataset, processing data, and evaluating results.
Map handling framework for automated driving
Bonnet and then some! Deep Learning Framework for various Image Recognition Tasks. Photogrammetry and Robotics Lab, University of Bonn
Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)
Implementation of the "LiDAR point clouds correction acquired from a moving car based on CAN-bus data" paper by Merriaux et al.
ROS driver for NovAtel GPS / GNSS receivers
LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking
Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups. ROS Package.
A Modular Optimization framework for Localization and mApping (MOLA)
The LiDAR segmenters library, for segmentation-based detection.
Tracking objects obtaining from segmentor and improve segmentation. https://github.com/LidarPerception/segmenters_lib
Package for implementing the Hungarian algorithm in C++. Also includes an attempt to speed up the process for large, sparse matching problems. These problems occur in particle physics, for which th…
The dependent Dirichlet process mixture of objects (DDPMO) model for detection-free tracking and object modeling.
Hierarchical Dirichlet processes. Topic models where the data determine the number of topics. This implements Gibbs sampling.
Dirichlet process mixture model code in Matlab. Sampling and variational.