Adaptive global visibility sampling

Abstract

In this paper we propose a global visibility algorithm which computes from-region visibility for all view cells simultaneously in a progressive manner. We cast rays to sample visibility interactions and use the information carried by a ray for all view cells it intersects. The main contribution of the paper is a set of adaptive sampling strategies based on ray mutations that exploit the spatial coherence of visibility. Our method achieves more than an order of magnitude speedup compared to per-view cell sampling. This provides a practical solution to visibility preprocessing and also enables a new type of interactive visibility analysis application, where it is possible to quickly inspect and modify a coarse global visibility solution that is constantly refined.

Visibility-Based R-Tree Spatial Index for Consistent Visualization in Indoor and Outdoor Scenes

“…The utilization of ray detection for visible set sampling is susceptible to erroneous judgments. By incorporating the calculation of spatial occupancy of visible objects [27] and grid-based preprocessing techniques [28], a harmonious balance between accuracy and efficiency is achieved. In recent years, Wang proposed utilizing grid predictive visibility for geospatial data scheduling, facilitating threedimensional navigation and visualization integration both indoors and outdoors within smart cities [29].…”