Automatic Extraction of Discontinuity Traces from Rock Mass Point Cloud Data Using PhaseVote

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Abstract
Accurate and thorough detection of rock mass discontinuities is essential in geotechnical engineering as they have a significant impact on the stability of underground and surface excavations, yet they are difficult to automate. In the work reported here, an automatic algorithm, termed PhaseVote, is proposed for the extraction of discontinuity traces from the colour (RGB) attributes of 3D meshes which, combined with the region growing method, can help derive more accurate discontinuity modelling of a rock mass. Conventional edge detection methods degrade under low contrast and variable illumination, and geometry-based point-cloud methods overlook spectral features. The proposed approach uses contrast-invariant phase-based feature detection. The detected edges are filtered and then extended or pruned to form major discontinuity traces using the tensor voting method. The first algorithm, phase analysis, detects edges independently of local contrast, while the latter, tensor voting, links and denoises the resultant edge fragments into continuous traces using perceptual grouping. The detection capability of the proposed method is assessed by applying it to two case studies and comparing the results with an existing method. PhaseVote achieved detection rates of 94.85% and 84.72% and IoUs of 70.27% and 69.80%, compared with 76.12%/45.95% and 69.81%/43.81% for a conventional existing method. The results demonstrate that the proposed method is resilient to image contrast, robust to changes in the orientation of discontinuity planes, is less subjective and has a higher degree of automation.

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