Micro-structural attributes of Chumathang granite from Leh, India, were experimentally determined in the temperature range from 25 to 600 °C for enhanced geothermal systems (EGS). P-wave velocity, thermal crack generation, and pore attributes were analyzed using a combination of pulse ultrasonic velocity study, 3D X-ray tomography and low-pressure gas adsorption experiments, respectively. Results indicate that thermal crack development is driven by mineral composition and differential thermal expansion, with a significant increase in the thermal damage factor between 450 ∘C and 600 ∘C, accompanied by visible cracks at 600 ∘C. Surface area and pore volume decreased up to 300 ∘C due to mineral dissolution, then slightly increased up to 600 ∘C due to microfracture formation. Pore size distribution showed a dominance of coarser mesopores, and fractal dimensions decreased with temperature, reflecting simpler pore geometries. These findings enhance the understanding of granite’s microstructural changes under thermal stress, informing the optimization of EGS heat extraction efficiency.

During a surface acquisition process using 3D scanners, noise is inevitable and an important step in geometry processing is to remove these noise components from these surfaces (given as point set or triangulated mesh). The noise removal process (denoising) can be performed by filtering the surface normals first and by adjusting the vertex positions according to filtered normals afterward. Therefore, in many available denoising algorithms, the computation of noise-free normals is a key factor. A variety of filters have been introduced for noise removal from normals, with different focus points like robustness against outliers or large amplitude of noise. Although these filters are performing well in different aspects, a unified framework is missing to establish the relation between them and to provide a theoretical analysis beyond the performance of each method.

In this paper, we introduce such a framework to establish relations between a number of widely used nonlinear filters for face normals in mesh denoising and vertex normals in point set denoising. We cover robust statistical estimation with M-smoothers and their application to linear and nonlinear normal filtering. Although these methods originate in different mathematical theories—which include diffusion-, bilateral-, and directional curvature-based algorithms—we demonstrate that all of them can be cast into a unified framework of robust statistics using robust error norms and their corresponding influence functions. This unification contributes to a better understanding of the individual methods and their relations with each other. Furthermore, the presented framework provides a platform for new techniques to combine the advantages of known filters and to compare them with available methods.