1. 22 Jul, 2014 4 commits
  2. 19 Jul, 2014 1 commit
    • Andreas Schwarzkopf's avatar
      [ADD ##371] Surface detection approach of Lari/Habib 2014 · 570f0b11
      Andreas Schwarzkopf authored
       * This approach describes the surface detection of Zahra Lari and Ayman Habib (2014).
       * Paper: "An adaptive approach for the segmentation and extraction of planar
       * andlinear/cylindrical features from laser scanning data", ISPRS Journal of
       * Photogrammetry and Remote Sensing, 2014
       * Eventually the process covers also the linear and cylindrical feature segmentation
       * but in our requirements only the planar segmentation is required. Steps:
       *   - Original laser point cloud as input data
       *   - PCA-based classification of planar features
       *   - Selection of appropriate representation models for the detected planar features
       *   - Local point density estimation along the detected planar features
       *   - Precise estimation of the planar features segmentation attributes
       *   - Parameter-domain segmentation and extraction of planar features
       *   - Boundary tracking for resolving the parameter-domain segmentation ambiguities of
       *     planar features
       *   - Extracted planar features in spatial domain
       * Estimations (Based on the paper because I haven't completely impelemented yet):
       *   - Fair segmentation results
       *   - Relatively slow
       * Implementation progress:
       *   - Done to the step "Precise estimation of the planar features segmentation
       *     attributes".
       * Other updates:
       *   - Renamed module "Pooints - Crop" to "Points - Transform". It optained the feature
       *     to translate and to rotate the point set.
       *   - Wew code refactoring.
  3. 15 Jul, 2014 2 commits
  4. 20 Jun, 2014 1 commit
    • Andreas Schwarzkopf's avatar
      [ADD ##368] Surface detection using the Region Growing Segmentation of the Point Cloud Library · c2b92c4b
      Andreas Schwarzkopf authored
       * Currently the module Surface detection by PCL only gives the ability to detect
       * surfaces using point clouds (WDataSetPoints) what is a method of a fair quality.
       * Some few changes across other modules:
       *   - Improvement on the octree neighbor search algorithm. Now you can look for
       *     neighbors using the neighborship of 6, 18 or 26
       *   - Very first steps on the surface detection algorithm "An adaptive approach for the
       *     segmentation and extraction of planar and linear/cylindrical features from laser
       *     scanning data" of Lari/Habib (2014).
       *   - Added an unidimensional kd tree structures with some processing methods such as
       *     looking for nearest nghbors.
  5. 21 May, 2014 1 commit
  6. 19 May, 2014 2 commits
  7. 16 May, 2014 3 commits
  8. 06 May, 2014 1 commit
    • aschwarzkopf's avatar
      [ADD #361] Added feature to delete small connected voxel groups · df86ea5c
      aschwarzkopf authored
       * The algorithm is intended to grouup voxels that have surfaces with the same
       * normal vector. This feature helps to remove small groups. Obviously trees
       * by nature have points that lead the Principal Component Analysis to
       * surfaces that have very different Eigen Vectors of the smallest Eigen Value
       * that is the normal vector in the case of surfaces.
       * Other feature: Removing voxels by a point count per voxel.
  9. 05 May, 2014 2 commits
    • aschwarzkopf's avatar
      [ADD 361] Prototypic surface detection OpenWalnut module · 7180d123
      aschwarzkopf authored
       * The new module firstly puts point data into a voxel grid. For each of these areas
       * the Principal Component Analysis is run. For each voxel the Eigen Vector of the
       * smallest Eigen Value is taken. In the case of surface kins data it's the normal
       * vector. In the last steps voxels with the similar vector are grouped.
       * Currently the region growing often bleeds over through many surfaces.
    • aschwarzkopf's avatar
      [ADD #360] Added prototypic module to remove tree structures. · 41b8f04c
      aschwarzkopf authored
       * This algorithm puts input point coulds to a voxel grid and applies the Principal
       * Component Analysis to each of these areas. It's calculated how isotropic voxels are.
       * Either planar voxels have the blue color and isotropic ones are red. There's a
       * feature to cut avay voxels from the output triangle mesh for the view. You can either
       * set the minimal point count per voxel or the quotient of the smallest Eigen value over
       * the biggest one as the maximal isotropic level criteria.
  10. 19 Apr, 2014 1 commit
  11. 16 Apr, 2014 1 commit
  12. 18 Mar, 2014 2 commits
  13. 03 Mar, 2014 1 commit
  14. 02 Mar, 2014 2 commits
  15. 19 Feb, 2014 1 commit
    • aschwarzkopf's avatar
      [ADD #309] Added some point output features and bugfixes. · 258944b5
      aschwarzkopf authored
      Following bugfixes new features:
        - Points group selector plugin: Can output selected buildings as
          WDataSetPointsGrouped in order to be able to view data in other plug-ins
        - Elevation image export plugin: Features to switch elevation display
          in triangle mesh height and color.
        - Fixes in documentation and few code cleanup.
  16. 18 Feb, 2014 1 commit
    • aschwarzkopf's avatar
      [ADD #309] Added colors to building point groups and direct elevation image display. · c04b1e87
      aschwarzkopf authored
      The buildings detection plugin generally was a mix of many things. These things of
      it were split up into these plutins:
        - Elevation image export
        - Points group selector which transforms building groups into a voxel structure
          and exports it to a triangle mesh.
      Added features:
        - Elevation image can be output in a triangle mesh
        - Elevation image (file and triangle mesh output) and building outline got colored
          building outline
      Code style:
        - Fixed prooblems with documentation (make doc)
        - Purged last code style (make stylecheck)
  17. 20 Jan, 2014 4 commits
  18. 16 Jan, 2014 1 commit
  19. 07 Jan, 2014 2 commits
  20. 05 Jan, 2014 1 commit
    • aschwarzkopf's avatar
      [ADD #309] Added Simplistic Building detection feature. · 9006f304
      aschwarzkopf authored
      Some simplistic algorithms are done in order to group buildings. Evaluation:
        + Important data structures as Octree and Quadtree are implemented with important functions
        + Grouping neighbor voxels runs even if still not 26-neighborship (few steps required)
           + Algorithm can easily be transferred to the quadtree set.
        + First new plugin for cutting of outlier points using the algorithm that calculates
          figuring out cube connectivity. Other points are cut off that don't belong to the
          largest voxel group
        - Possibly vaporized effort
            - The algorithm that takes relative elevation minimums has problems detecting low buildings.
                - Probably the algorithm won't be taken
                + The great deal of the code amount used for it can be very useful for the final code.
            + It's easy to differ trees from buildings
                - Not thought to the end to differ trees that are very close to buildings
  21. 02 Jan, 2014 1 commit
  22. 13 Dec, 2013 2 commits
  23. 11 Dec, 2013 3 commits
    • aschwarzkopf's avatar
      #309 · 29202dd3
      aschwarzkopf authored
      Removing a file that I've created by mistake
    • aschwarzkopf's avatar
      #309 · de505209
      aschwarzkopf authored
      Done few very first steps for building detection:
        - Areas that contain data set points, are outlined by octree nodes
        - Ability to draw that outline as boxes in order to be able to draw very
          huge las files at all.
    • aschwarzkopf's avatar
      #309 · 6cb6f85a
      aschwarzkopf authored
      Just started very first building detection implementation steps:
        - Transforming point data to octree nodes
        - Ability to display LAS data as boxes, but without color intensity display.
          Now you don't need vast amount of RAM (32GB for a 500MB las file) and GPU
          memory to be able to display larger las files at all.