All Projects → yzrobot → Cloud_annotation_tool

yzrobot / Cloud_annotation_tool

Licence: gpl-3.0
L-CAS 3D Point Cloud Annotation Tool

Projects that are alternatives of or similar to Cloud annotation tool

pcl-edge-detection
Edge-detection application with PointCloud Library
Stars: ✭ 32 (-82.42%)
Mutual labels:  point-cloud, pcl
3d Bat
3D Bounding Box Annotation Tool (3D-BAT) Point cloud and Image Labeling
Stars: ✭ 179 (-1.65%)
Mutual labels:  point-cloud, annotation
persee-depth-image-server
Stream openni2 depth images over the network
Stars: ✭ 21 (-88.46%)
Mutual labels:  point-cloud, pcl
Pclpy
Python bindings for the Point Cloud Library (PCL)
Stars: ✭ 212 (+16.48%)
Mutual labels:  point-cloud, pcl
Pcl
Point Cloud Library (PCL)
Stars: ✭ 6,897 (+3689.56%)
Mutual labels:  point-cloud, pcl
pcl.py
Templated python inferface for Point Cloud Library (PCL) based on Cython
Stars: ✭ 64 (-64.84%)
Mutual labels:  point-cloud, pcl
pcljava
A port of the Point Cloud Library (PCL) using Java Native Interface (JNI).
Stars: ✭ 19 (-89.56%)
Mutual labels:  point-cloud, pcl
annotate
Create 3D labelled bounding boxes in RViz
Stars: ✭ 104 (-42.86%)
Mutual labels:  annotation, pcl
Depth clustering
🚕 Fast and robust clustering of point clouds generated with a Velodyne sensor.
Stars: ✭ 657 (+260.99%)
Mutual labels:  point-cloud, pcl
Fast gicp
A collection of GICP-based fast point cloud registration algorithms
Stars: ✭ 307 (+68.68%)
Mutual labels:  point-cloud, pcl
cloud to map
Algorithm that converts point cloud data into an occupancy grid
Stars: ✭ 26 (-85.71%)
Mutual labels:  point-cloud, pcl
Pcl Ros Cluster Segmentation
Cluster based segmentation of Point Cloud with PCL lib in ROS
Stars: ✭ 123 (-32.42%)
Mutual labels:  point-cloud, pcl
Ndt omp
Multi-threaded and SSE friendly NDT algorithm
Stars: ✭ 291 (+59.89%)
Mutual labels:  point-cloud, pcl
Point Cloud Filter
Scripts showcasing filtering techniques applied to point cloud data.
Stars: ✭ 34 (-81.32%)
Mutual labels:  point-cloud, pcl
Lidar camera calibration
Light-weight camera LiDAR calibration package for ROS using OpenCV and PCL (PnP + LM optimization)
Stars: ✭ 133 (-26.92%)
Mutual labels:  point-cloud, pcl
Pointasnl
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling (CVPR 2020)
Stars: ✭ 159 (-12.64%)
Mutual labels:  point-cloud
Torchsparse
A high-performance neural network library for point cloud processing.
Stars: ✭ 173 (-4.95%)
Mutual labels:  point-cloud
Controllerextrabundle
Controller extra Bundle for Symfony2
Stars: ✭ 157 (-13.74%)
Mutual labels:  annotation
Pdfanno
Linguistic Annotation and Visualization Tool for PDF Documents
Stars: ✭ 156 (-14.29%)
Mutual labels:  annotation
Displaz
A hackable lidar viewer
Stars: ✭ 177 (-2.75%)
Mutual labels:  point-cloud

Notices

L-CAS 3D Point Cloud Annotation Tool

Build Status Codacy Badge License: GPL v3

screenshot

The tool provides a semi-automatic labeling function, means the 3D point cloud data (loaded from the PCD file) is first clustered to provide candidates for labeling, each candidate being a point cluster. Then, the user annotating the data, can label each object by indicating candidate's ID, category, and visibility. A flowchart of this process is shown below.

flowchart

The quickest way to activate the optional steps is to modify the source code and recompile. 😱

Compiling (tested on Ubuntu 14.04/16.04)

Prerequisites

  • Qt 4.x: sudo apt-get install libqt4-dev qt4-qmake
  • VTK 5.x: sudo apt-get install libvtk5-dev
  • PCL 1.7: sudo apt-get install libpcl-1.7-all-dev

Build and run

  • mkdir build
  • cd build
  • cmake ..
  • make
  • ./cloud_annotation_tool

Test examples

lcas_simple_data.zip contains 172 consecutive frames (in .pcd file) with 2 fully annotated pedestrians.

Citation

If you are considering using this tool and the data provided, please reference the following:

@article{yz19auro,
   author = {Zhi Yan and Tom Duckett and Nicola Bellotto},
   title = {Online learning for 3D LiDAR-based human detection: Experimental analysis of point cloud clustering and classification methods},
   journal = {Autonomous Robots},
   year = {2019}
}
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].