3D reconstruction system to creating detailed scene geometry from range video.
LATEST NEWS (7/22/2015):
We have published my fork of PCL. It is a development version, for reference only. We don't provide any support. https://github.com/qianyizh/StanfordPCL
Executable system available at http://redwood-data.org/indoor/tutorial.html
Lots of useful things - software, data, evaluation tools, beautiful videos and pictures - are on: Project page: http://qianyi.info/scene.html New project page: http://redwood-data.org/indoor/
This is an open source C++ implementation based on the technique presented in the following papers:
Robust Reconstruction of Indoor Scenes, CVPR 2015 Sungjoon Choi, Qian-Yi Zhou, and Vladlen Koltun
Simultaneous Localization and Calibration: Self-Calibration of Consumer Depth Cameras, CVPR 2014 Qian-Yi Zhou and Vladlen Koltun
Elastic Fragments for Dense Scene Reconstruction, ICCV 2013 Qian-Yi Zhou, Stephen Miller and Vladlen Koltun
Dense Scene Reconstruction with Points of Interest, SIGGRAPH 2013 Qian-Yi Zhou and Vladlen Koltun
Project pages: http://qianyi.info/scene.html http://redwood-data.org/indoor/
Executable system: http://redwood-data.org/indoor/tutorial.html
Data: http://qianyi.info/scenedata.html http://redwood-data.org/indoor/dataset.html
Citation instructions: http://redwood-data.org/indoor/pipeline.html
The source code is released under MIT license.
In general, you can do anything with the code for any purposes, with proper attribution. If you do something interesting with the code, we'll be happy to know about it. Feel free to contact us.
We include code and libraries for some software not written by us, to ensure easy compilation of the system. You should be aware that they can be released under different licenses:
g2o - BSD license vertigo - GPLv3 license SuiteSparse - LGPL3+ license Eigen - MPL2 license
GlobalRegistration A state-of-the-art global registration algorithm that aligns point clouds together.
GraphOptimizer Pose graph optimization that prunes false global registration results. See CVPR 2015 paper for details.
FragmentOptimizer The core function that simultaneously optimizes point cloud poses and a nonrigid correction pattern. See CVPR 2014 and ICCV 2013 papers for details.
BuildCorrespondence ICP refinement for point cloud pairs registered by GlobalRegistration module.
Integrate A CPU-based algorithm that integrates depth images into a voxel, based on camera pose trajectory and nonrigid correction produced by previous steps.
Matlab_Toolbox A Matlab toobox for evaluation of camera pose trajectory and global registration.
In the executable package
See tutorial on this page: http://redwood-data.org/indoor/tutorial.html
We strongly recommend you compile Point Cloud Library (PCL) x64 with Visual Studio. http://pointclouds.org/
SuiteSparse is required for solving large sparse matrices. https://github.com/PetterS/CXSparse
ACML is required for SuiteSparse. http://developer.amd.com/tools-and-sdks/cpu-development/amd-core-math-library-acml/
The compilation requires Visual Studio 2010 on a Windows 7/8.1 64bit system.
We are not happy with the current compatibility issues. We are working on a new code release that will not depend on external libraries as much and will be much easier to compile. Stay tuned.