CuFusion

This repo is a fork of StanfordPCL(Qianyi Zhou's PCL Fork) containing the code implementation of our work CuFusion, a novel approach for accurate real-time depth camera tracking and volumetric scene reconstruction with a known cuboid reference object.

This is the initial version of our algorithm with trial dirty code and redundant comments.

Dataset

We introduce a dataset called CU3D, for the validation of our algorithm.

The dataset contains 3 noiseless synthetic sequences with both the ground-truth (GT) camera trajectories and GT mesh scene models, and 6 noisy real-world scanning sequences with ONLY the GT mesh models of the scanned objectives.

The synthetic data are generated with camera intrinsic $K_s$ (synDep525.param), while the real-world data are with intrinsic $K_r$ (myKinectV1.param).

Note: Different from the TUM RGB-D dataset, where the depth images are scaled by a factor of 5000, currently our depth values are stored in the PNG files in millimeters, namely, with a scale factor of 1000. We may remake the data to conform to the style of the TUM dataset later.

Related Publications

If you find this code or CU3D dataset valuable for your research please cite this work:

Zhang, C.; Hu, Y. CuFusion: Accurate Real‐time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid. Preprints 2017, 2017080022 (doi: 10.20944/preprints201708.0022.v1).

How to build

We've tested our code on Windows 10, with Visual Studio 2010 (Though other configurations may work) To build this repo from source, you should follow the instructions from PCL, e.g., Compiling PCL from source on Windows. If you've already built/installed PCL, you need to build the pcl_gpu_kinfu_large_scale library and pcl_kinfu_largeScale executable to run our algorithm.

Additional prerequisites:

• PEAC: our fork of the work of Chen Feng. Clone our repo and add the source code manually to pcl_kinfu_largeScale and pcl_gpu_kinfu_large_scale to avoid compilation errors.
• OpenCV: for image processing; we tested our code with version 2.4.x;
• Cuda

How to use

1. Our algorithm

To test our algorithm, run pcl_kinfu_largeScale_release.exe in command line:

pcl_kinfu_largeScale_release.exe --shift_z -0.3 -cusz 400,300,250 -e2cDist 0.05 -trunc_dist 0.025 -vs 1 -tsdfVer 11 --camera myKinectV1.param -eval "04-lambunny\"

Params explanation:

• --shift_z <in_meters>: initial shift along z axis
• -cusz <a,b,c>: toggle to use our algorithm, with the reference cuboid size being a*b*c millimeters
• -e2cDist <in_meters>: edge-to-edge distance threshold for correspondence matching
• -trunc_dist <in_meters>: truncation distance of the TSDF
• -vs <in_meters>: volume size of the reconstruction
• -tsdfVer <version number>: different versions of our TSDF fusion strategy, currently stable with version 11
• --camera <param_file>: launch parameters from the file, containing the camera intrinsic matrix
• -eval <eval_folder>: folder containing the evaluation dataset.

2. Algorithms for comparison in our work

2.1. KinectFusion [1]

pcl_kinfu_largeScale_release.exe  --shift_z -0.3 -trunc_dist 0.025 -vs 1 --camera myKinectV1.param -eval E:\geo-cu399-pen\turntable\antenna2c-raw_frames-ts

2.2. Zhou et al. [2]

pcl_kinfu_largeScale_release.exe --shift_z -0.3 --bdr_odometry -trunc_dist 0.025 -vs 1 --camera myKinectV1.param -eval "04-lambunny\"

2.3. ElasticFusion [3]

To test this algorithm, we need to convert the image sequences to *.klg files. We use this code for such work, build ElasticFusion from source, and run it.

References

1. Newcombe, R. A.; Izadi, S.; Hilliges, O.; Molyneaux, D.; Kim, D.; Davison, A. J.; Kohli, P.; Shotton, J.; Hodges, S.; Fitzgibbon, A. KinectFusion: Real-time Dense Surface Mapping and Tracking. In Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality; ISMAR ’11; IEEE Computer Society: Washington, DC, USA, 2011; pp. 127–136.
2. Zhou, Q.-Y.; Koltun, V. Depth camera tracking with contour cues. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2015; pp. 632–638.
3. Whelan, T.; Leutenegger, S.; Moreno, R. S.; Glocker, B.; Davison, A. ElasticFusion: Dense SLAM Without A Pose Graph. In Proceedings of Robotics: Science and Systems; Rome, Italy, 2015.

Original README.txt of StanfordPCL

=============================================================================== = Qianyi Zhou's PCL Fork = +==============================================================================

I have been receiving requests for the source code of pcl_kinfu_largeScale_release.exe, which is a critical module in the robust scene reconstruction system we have developed.

Project: http://qianyi.info/scene.html Code: https://github.com/qianyizh/ElasticReconstruction Executable system: http://redwood-data.org/indoor/tutorial.html

Thus I publish my fork of PCL here as a reference.

===============================================================================

Disclaimer

I forked PCL from an early development version three years ago, and have made numerous changes. This repository is an image of my personal development chunk. It contains many experimental functions and redundant code.

THERE IS NO GUARANTEE THIS CODE CAN BE COMPILED OR USED. WE DO NOT INTEND TO PROVIDE ANY SUPPORT FOR THIS CODE. IT SHOULD BE USED ONLY AS A REFERENCE.

If you are looking for the official PCL, go to this repository: https://github.com/PointCloudLibrary/pcl

===============================================================================

License

As part of the scene reconstruction system, the code of this repository is released under the MIT license.

Some parts of this repository are from different libraries:

Original PCL - BSD license SuiteSparse - LGPL3+ license (we have a static library linked by Visual Studio) Eigen - MPL2 license (we have a copy of a certain version of Eigen)