The 3D modeling of heritage objects is now regarded as a significant aid and has become essential in the study and mediation of cultural heritage. In horology, 3D models serve not only for visualization but also to simulate complex movements, replicate mechanisms, and study their functionality for scientific and educational purposes. While promising, modeling accurate functional 3D clock models presents challenges that require considerable time and expertise in both clockmaking and mechanical modeling. This paper presents a new method to automatically generate functional mechanical clock assemblies given a formalized interaction graph. The latter describes both the parts of the mechanism and the sub-assemblies, all respecting constraints defined with the help of experts in the field. This graph is then used as a base for our novel parameter propagation algorithm to automatically generate valid 3D models of the corresponding clock from a limited amount of input data. Our method provides different generations of clocks that adhere to functional constraints such as the mechanism category (e.g. comtoise, Neuchâteloise, Paris...), gear ratios or part placement. • An automatic generation process to produce functional 3D models of clocks from a minimal set of input parameters. • A formalized abstract mechanism representation called the “interaction graph”. • Horology cultural heritage application.

Reflectance models capture many types of visual appearances. The most plausible reflectance models follow the Microfacet theory, which is specifically based on statistical representations, with an analytic visibility term. This visibility term has a significant impact on appearance. Visibility computed with the masking term proposed by Smith (1967), and revisited by Ashikhmin et al. (2000), is nowadays considered as the most plausible in the literature. It is simple and efficient to evaluate for statistical distributions, but it relies on assumptions that are not necessarily respected by real surfaces. This article proposes an in-depth study of masking for meshed height-field surfaces, generated either from measured real-world materials or from functions derived from distributions of surface normals. We experimentally estimate the masking (and shadowing) of surfaces using a ray-casting technique, and compare their measurements with the theoretical model from Smith and Ashikhmin et al. We show that their assumptions are too restrictive for a majority of real-world surfaces. We propose a model capable of predicting how close the theoretical masking term can be from the masking term estimated by a ray-casting approach. Although most surfaces break their assumptions, our results show that the term from Smith and Ashikhmin et al. can still be reasonably employed for a fraction in a set of more than 400 measured surfaces, with low errors compared to a ray-casting masking estimation, much lower computation times, and very similar visual appearances. Our model can be used to predict the incurred error on a physically-based rendering simulation with a microfacet-based BRDF created from real-world surfaces, instead of explicitly calculating the masking term from its height field.

Abstract Man‐made objects commonly exhibit rounded edges and corners generated through their manufacturing processes. The variation of surface normals at these confined locations produces shading details that are visually essential to the realism of synthetic scenes. The more specular the surface, the finer and more prominent its highlights. However, most geometric modellers represent rounded edges and corners with dense polygonal meshes that are limited in terms of smoothness, while tremendously increasing scene complexity. This paper proposes a non‐invasive method (i.e. that does not modify the original geometry) for the modelling and rendering of smooth edges and corners from any input polygonal geometry defined with infinitely sharp edges. At the heart of our contribution is a geometric structure that automatically and accurately defines the geometry of edge and corner rounded areas, as well as the topological relationships at edges and vertices. This structure, called SREC‐RT, is integrated in a ray‐tracing‐based acceleration structure in order to determine the region of interest of each rounded edge and corner. It allows systematic rounding of all edges and vertices without increasing the 3D scene geometric complexity. While the underlying rounded geometry can be of any type, we propose a practical ray‐edge and ray‐corner intersection based on parametric surfaces. We analyse comparisons generated with existing methods. Our results present the advantages of our method, including extreme close‐up views of surfaces with a much higher quality for very little additional memory, and reasonable computation time overhead.

Abstract This paper explores constrained convex space partition (CCSP) as a new acceleration structure for ray tracing. A CCSP is a graph, representing a space partition made up of empty convex volumes. The scene geometry is located on the boundary of the convex volumes. Therefore, each empty volume is bounded with two kinds of faces: occlusive ones (belonging to the scene geometry), and non‐occlusive ones. Given a ray, ray casting is performed by traversing the CCSP one volume at a time, until it hits the scene geometry. In this paper, this idea is applied to architectural scenes. We show that CCSP allows to cast several hundreds of millions of rays per second, even if they are not spatially coherent. Experiments are performed for large furnished buildings made up of hundreds of millions of polygons and containing thousands of light sources.

This paper describes a new scheme for computing out-of-core global illumination in complex indoor scenes using a photon-mapping approach. Our method makes use of a cells-and-portals representation of the environment for preserving memory coherence and storing rays or photons. We have successfully applied our method to various buildings, composed of up to one billion triangles. As shown in the results, our method requires only a few hundred megabytes of memory for tracing more than 1.6 billion photons in large buildings.