An analysis of extracting cross sections from heritage site point clouds using meshfree point-based techniques
In the digital age cultural heritage has evolved to incorporate 3D virtual models of heritage buildings and sites. It is common to use laser scanning to acquire the 3D point cloud data for these models, with this data usually being processed and “meshed” to form a surface mesh model, however, this process is computationally costly and generally creates new data (beyond the original, correct, surface samples). For heritage conservation purposes, accurate and original data is preferred. An important issue when using the points directly, rather than the 3D surface model, is the suitability of a direct point representation for rendering and image processing. One important task is the production of floorplan, elevation, and cross-section views from a 3D digital heritage model of a site. In this work, the viability of a Point Based Technique (PBT) approach, for cross-section recovery from heritage point cloud models, is evaluated. The solution developed involves slicing the 3D point cloud, applying binary morphology operations on this 2D point cloud slice to close gaps in the cross-section profile, and filling holes in the cross-section profile. Post processing operations add minor but necessary improvements (such as filling gaps on the ground level due to slicing through vegetation and removing noise). Finally, to produce the desired image, the cross-section is overlaid onto the rendering of the scene (removing points in front of the cross-section slicing plane). The results are assessed by registering the point-based cross-section against the meshbased equivalent. The Intersection over Union (IoU) metric, a measure of similarity between two images, is calculated on the registered images. The tabulated IoU and IoU loss (dissimilarity between the registered cross-sections) is also depicted graphically. The conclusion drawn on analysing the results, is that the fidelity of the point-based cross-section is a close approximation to the mesh-based cross-section (even when considering possible errors from manual registration of the cross-sections), thus validating the point based approach. The average IoU “score” across the 24 crosssections recovered from 4 point cloud, cultural heritage models is 94.6%. An argument may be made for the point-based cross-section being an improvement over the meshbased cross-section, where the latter connects points incorrectly in the mesh reconstruction of the cross-section. A positive relationship is found between the point density and the fidelity of the pointbased cross-section. An examination of the data and results reveals that a higher point density relates to a higher fidelity of the cross-section. The cross-section profile in a higher density point cloud is more likely to have no gaps in the profile, after binary morphology Closing (distance between points are smaller at higher point densities). Note that high point density is typical of current scanning technology (LIDAR, Photogrammetry) but the output is typically downsampled for practical use (e.g. uploading to repositories with file size limitations, to make the data publicly available), relying instead on mesh reconstruction to model a surface. For the task of recovering cross-sections from 3D point clouds of cultural heritage sites we conclude that point- based techniques offer a similar accuracy to mesh-based techniques - provided that the model possesses a sufficiently high point density. The advantage of using points directly is the avoidance of (i) additional effort and computational cost of reconstructing a mesh, and (ii) the possible creation of undesirable “new” points (in addition to the original points) in the mesh reconstruction process.
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