PCF - Point Cloud Fit

     ______  ______         ______  __  _______
    /  _  / / ____/  ___   / ____/ / / /__  __/
   / ____/ / /___   /__/  / ___/  / /    / /
  /_/     /_____/        /_/     /_/    /_/

PCF - Point Cloud Fit, is a robust automatic detection scheme for geometric primitives such as planes, cylinders and cuboids.

In this scheme, cylinders are first detected in iteration of energy-based geometric model fitting (by GCO) and cylinder parameter estimation (by GTEngine). Planes are then detected by Hough transform, and further described as bounded patches with their minimum bounding rectangles. Cuboids are finally detected with pair-wise geometry relations from the detected patches. After successively detection of cylinders, planar patches and cuboids, a mid-level geometry representation can be delivered.

The detection results can be observed by rendering in 3D model view tool PlyWin.

Projects

• Obj2Ply : sampling the mesh model data (*.obj) into point cloud model data (*.ply).

• PCE : the robust automatic detection scheme. This scheme takes the point cloud model *.ply as input and finally output the *.strcut file in which the detected components are recorded.

• Struct2Ply : sampling the the detected components (*.strcut) into point cloud model data (*.ply).

The accuracy and completeness estimations for 3D reconstruction can be utilized here to quantitatively evaluate the accuracy of the detection results. Project Struct2Ply can offer you a convenience to convert the detection results into point cloud models, and the accuracy, completeness and F-score can then be estimated with PCE.

Dependencies

• Qt
• VCG Library (Embedded)
• GCO (Embedded)
• GTEngine (Embedded)

Examples

Detection results of the test data:

Publications

This program and data in TestData have been used in the following publications:

@article{weiSensors18,
  author  = {Quanmao Wei and Zhiguo Jiang and Haopeng Zhang},
  title   = {Robust Spacecraft Component Detection in Point Clouds},
  journal = {Sensors},
  volume  = {18},
  year    = {2018},
  number  = {4},
  article number = {933},
  url     = {http://www.mdpi.com/1424-8220/18/4/933},
  issn    = {1424-8220},
  doi     = {10.3390/s18040933}
}

@inproceedings{weiIGTA17,
  author    = {Quanmao Wei and Zhiguo Jiang and Haopeng Zhang and Shanlan Nie},
  title     = {Spacecraft Component Detection in Point Clouds},
  booktitle = {Advances in Image and Graphics Technologies},
  year      = {2017},
  publisher = {Springer Singapore},
  address   = {Singapore},
  pages     = {210--218},
  isbn      = {978-981-10-7389-2}
}

By WeiQM at D409.IPC.BUAA