Visual enhancement of 3D images of rock faces for fracture mapping

As an alternative to simple decimation and 2.5D Delaunay triangulation, this paper introduces methodologies known as epsilon-nets and Poisson surface reconstruction to reduce the size of large point clouds of geological scenes and generate a mesh. Epsilon-nets produce decimated point clouds free from undesirable organized patterns linked to data acquisition. Through the ε parameter, the user specifies the number of selected points which are then meshed in 3D by the Poisson surface reconstruction algorithm. Poisson meshes capture the topology of the rock face with a high fidelity and are robust enough to handle a degree of occlusion. These methodologies are demonstrated using an image of a rock face featuring large cubic blocks acquired at a camera-target distance of 3 m and containing 2044,140 points. The point cloud was decimated by a factor of 16 using an epsilon-net with ε≈0.0002, and the error, reported as the average Hausdorff distance (using the original point cloud as the source and the epsilon net as the target), was approximately 1 mm, which is on the same order of magnitude as the camera accuracy. Poisson surface reconstruction produced a realistic mesh and reduced noise, which conditioned the data for fracture analysis. A stereonet distinguished two sub-vertical joint sets that had been combined in previous studies. This case history is illustrated by color-coded displays of the strike and dip angles of mesh elements, highlighting ranges of angles of interest and capturing overhang areas.