Stars
[CVPR 2024 Highlight] FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
Official Implementation of CVPR24 highligt paper: Matching Anything by Segmenting Anything
MinkLoc3D: Point Cloud Based Large-Scale Place Recognition
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
A fast medical imaging analysis library in Python with algorithms for registration, segmentation, and more.
Catkin wrapper for a Bazel-free build of Tensorflow
FCSS (Fully Convolutional Self-Similarity) Descriptor, CVPR'2017, TPAMI'2019
gmu configuration of kitti and cartographer ros
Drivers for receiving LiDAR data and more, support Lidar Mid-40, Mid-70, Tele-15, Horizon, Avia.
Fast LOAM: Fast and Optimized Lidar Odometry And Mapping for indoor/outdoor localization IROS 2021
OpenMMLab's next-generation platform for general 3D object detection.
DASC (Dense Adaptive Self-Correlation) Descriptor, CVPR'2015, TPAMI'2017
[CVPR2020] Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation
Pytorch framework for doing deep learning on point clouds.
A PyTorch Library for Accelerating 3D Deep Learning Research
PyTorch3D is FAIR's library of reusable components for deep learning with 3D data
Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.
Experimental code to read/write NumPy .NPY files in MATLAB
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
A ROS sensor driver for the Azure Kinect Developer Kit.
Path Aggregation Network for Instance Segmentation
PANet for Instance Segmentation and Object Detection
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
Object detection and segmentation for a surgery robot using Mask-RCNN on Python 3, Keras, and TensorFlow..
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow