All Projects → c42f → Displaz

c42f / Displaz

Licence: other
A hackable lidar viewer

Projects that are alternatives of or similar to Displaz

Interactive slam
Interactive Map Correction for 3D Graph SLAM
Stars: ✭ 372 (+110.17%)
Mutual labels:  point-cloud, lidar
Hdl graph slam
3D LIDAR-based Graph SLAM
Stars: ✭ 945 (+433.9%)
Mutual labels:  point-cloud, lidar
Superpoint graph
Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
Stars: ✭ 533 (+201.13%)
Mutual labels:  point-cloud, lidar
BtcDet
Behind the Curtain: Learning Occluded Shapes for 3D Object Detection
Stars: ✭ 104 (-41.24%)
Mutual labels:  point-cloud, lidar
Awesome Robotic Tooling
Tooling for professional robotic development in C++ and Python with a touch of ROS, autonomous driving and aerospace.
Stars: ✭ 1,876 (+959.89%)
Mutual labels:  point-cloud, lidar
3d cnn tensorflow
KITTI data processing and 3D CNN for Vehicle Detection
Stars: ✭ 266 (+50.28%)
Mutual labels:  point-cloud, lidar
Lidar camera calibration
ROS package to find a rigid-body transformation between a LiDAR and a camera for "LiDAR-Camera Calibration using 3D-3D Point correspondences"
Stars: ✭ 734 (+314.69%)
Mutual labels:  point-cloud, lidar
point-cloud-prediction
Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks
Stars: ✭ 97 (-45.2%)
Mutual labels:  point-cloud, lidar
Laser Camera Calibration Toolbox
A Laser-Camera Calibration Toolbox extending from that at http://www.cs.cmu.edu/~ranjith/lcct.html
Stars: ✭ 99 (-44.07%)
Mutual labels:  point-cloud, lidar
Weakly Supervised 3d Object Detection
Weakly Supervised 3D Object Detection from Point Clouds (VS3D), ACM MM 2020
Stars: ✭ 61 (-65.54%)
Mutual labels:  point-cloud, lidar
awesome-lidar
😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.
Stars: ✭ 217 (+22.6%)
Mutual labels:  point-cloud, lidar
Extrinsic lidar camera calibration
This is a package for extrinsic calibration between a 3D LiDAR and a camera, described in paper: Improvements to Target-Based 3D LiDAR to Camera Calibration. This package is used for Cassie Blue's 3D LiDAR semantic mapping and automation.
Stars: ✭ 149 (-15.82%)
Mutual labels:  point-cloud, lidar
urban road filter
Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗
Stars: ✭ 134 (-24.29%)
Mutual labels:  point-cloud, lidar
Lidr
R package for airborne LiDAR data manipulation and visualisation for forestry application
Stars: ✭ 310 (+75.14%)
Mutual labels:  point-cloud, lidar
uav-lidar-analytics-course
NCSU GIS595/MEA792: UAV/lidar Data Analytics
Stars: ✭ 23 (-87.01%)
Mutual labels:  geospatial, lidar
Depth clustering
🚕 Fast and robust clustering of point clouds generated with a Velodyne sensor.
Stars: ✭ 657 (+271.19%)
Mutual labels:  point-cloud, lidar
FLAT
[ICCV2021 Oral] Fooling LiDAR by Attacking GPS Trajectory
Stars: ✭ 52 (-70.62%)
Mutual labels:  point-cloud, lidar
whiteboxgui
An interactive GUI for WhiteboxTools in a Jupyter-based environment
Stars: ✭ 94 (-46.89%)
Mutual labels:  geospatial, lidar
3d Tiles
Specification for streaming massive heterogeneous 3D geospatial datasets 🌎
Stars: ✭ 1,054 (+495.48%)
Mutual labels:  point-cloud, geospatial
Lidar camera calibration
Light-weight camera LiDAR calibration package for ROS using OpenCV and PCL (PnP + LM optimization)
Stars: ✭ 133 (-24.86%)
Mutual labels:  point-cloud, lidar

============================================== displaz - A viewer for geospatial point clouds

displaz is a cross platform viewer for displaying lidar point clouds and derived artifacts such as fitted meshes. The interface was originally developed for viewing large airborne laser scans, but also works quite well for point clouds acquired using terrestrial lidar and other sources such as bathymetric sonar.

The goal is to provide a flexible and programmable technical tool for exploring large lidar point data sets and derived geometry.

  • Open point clouds up to the size of main memory. Performance remains interactive as the number of points becomes too large to draw in a single frame.
  • Create custom point visualizations. The OpenGL shader can be edited interactively. In the shader program, you automatically have access to any per-point attributes defined in the input file. Shader parameters are connected to user-defined GUI controls.
  • Plot interactively from your favourite programming language. Displaz IPC lets you script the interface from the command line. Experimental language bindings are available for C++, python, julia and Matlab.

User guide

See the user guide <doc/userguide.rst>_ for usage examples and instructions.

Installation

Binary installer packages for windows are provided on the releases page <https://github.com/c42f/displaz/releases>_. For linux, it should be fairly easy to build it yourself by following the instructions below.

Building

Linux

Install dependencies using your package manager.  Here's a handy list of
dependencies for several distributions::

    # Ubuntu >= 14.04 (and probably other debian-based distributions)
    sudo apt-get install git g++ cmake qt5-default python-docutils

    # Mint
    sudo apt-get install git g++ cmake qt5-default libqt5opengl5-dev python-docutils

    # Older ubuntu (qt4 based - add cmake flag -DDISPLAZ_USE_QT4=TRUE)
    sudo apt-get install git g++ cmake libqt4-dev libqt4-opengl-dev python-docutils

    # Fedora 28
    sudo yum install git gcc-c++ make patch cmake qt5-qtbase-devel mesa-libGLU-devel python-docutils

    # OpenSuse
    sudo zypper install git gcc-c++ libqt5-qtbase-devel glu-devel python-docutils

The following commands may be used to build displaz on linux::

    # Get the source code
    git clone https://github.com/c42f/displaz.git
    cd displaz

    # Build LASlib and ilmbase
    mkdir build_external
    cd build_external
    cmake ../thirdparty/external
    make -j4
    cd ..

    # Build displaz
    mkdir build
    cd build
    cmake ..
    make -j4

    # Install into CMAKE_INSTALL_PREFIX=/usr/local
    sudo make install


Troubleshooting:

* Some people have had issues with a version of qt in their path clashing with
  the qt headers installed on the system. This may give an error such as
  "undefined reference to qt_version_tag", or some other qt library-related
  link error.  For example having the qt version distributed with the python
  package system ``conda`` has been known to cause issues, which can be solved
  by removing it from the ``$PATH`` variable *before* calling cmake in the
  script above.


Windows x64

The windows releases are built using cmake and Visual Studio <https://www.visualstudio.com/en-us/products/visual-studio-community-vs.aspx>_. To install the dependencies on windows, manually download and install the following tools:

  • cmake <http://www.cmake.org/download/>_
  • msysgit <https://msysgit.github.io/>_
  • qt5 <http://www.qt.io/download-open-source>_ (ensure you get the correct version for your compiler)
  • nsis <http://nsis.sourceforge.net/Download>_ (only required for installable package creation)

To build, first clone the repository using the msysgit command line::

# Get the source code
git clone https://github.com/c42f/displaz.git

You can build displaz with various supported cmake build system generators. For the continuous integration build (and probably future releases), the Visual Studio generator "Visual Studio 14 Win64" is used::

rem Build LASlib and ilmbase
mkdir build_external
cd build_external
cmake -G "Visual Studio 14 Win64" -D CMAKE_BUILD_TYPE=Release ..\thirdparty\external
cmake --build . --config Release
cd ..

rem Build displaz.
rem Assumes that Qt has been installed into C:\Qt\Qt5.5.1\5.5\msvc2015_64
mkdir build
cd build
cmake -G "Visual Studio 14 Win64" ^
    -D CMAKE_PREFIX_PATH=C:\Qt\Qt5.5.1\5.5\msvc2015_64 ^
    -D CMAKE_INSTALL_PREFIX:PATH=dist ^
    ..
cmake --build . --config Release

rem Optionally, create the installer package
cmake --build . --config Release --target package

Some of the cmake generators such as NMake Makefiles" won't find visual studio unless it's in the path. In that case you'd need to launch the steps above from the x64 cross tools command prompt.

OSX


TODO - for the moment see the generic build instructions below.  Also note that
displaz is regularly built on OSX via travis-CI, so the commands in the file
``.travis.yml`` in the repository should more or less work.


Generic build

To build displaz, install the following tools:

  • cmake >= 2.8.8
  • Python docutils (optional - required to build the html documentation)

Displaz also depends on several libraries. For simplicity, the smaller dependencies are bundled in the thirdparty directory. There's also an automated download/build system for some of the larger ones (LASlib and ilmbase) available at thirdparty/external/CMakeLists.txt. However, you will need to install the following manually:

  • Qt >= 5.0 (qt-4.8 is still semi-supported on linux)
  • OpenGL >= 3.2
  • ilmbase >= 1.0.1 (You don't need to install this if you're using the automated thirdparty build)

Both the LASlib and IlmBase libraries may be built using the separate third party build system in thirdparty/external/CMakeLists.txt.

Supported Systems

displaz is regularly compiled on linux, OSX and windows. It's known to work well with recent NVidia and ATI graphics cards and drivers. Some issues have been observed with Intel integrated graphics and older ATI drivers. If you observe rendering artifacts there's a reasonable chance that your graphics card or drivers are playing dirty tricks

Third party libraries used in displaz

Behind the scenes displaz uses code written by many people. The following third party projects are gratefully acknowledged:

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].