Architectural Co-LOD Generation

TL;DR

This paper introduces Co-LOD, a novel approach for managing levels of detail in architectural models that ensures semantic consistency and handles noisy inputs effectively.

Contribution

Co-LOD employs shape co-analysis to standardize structures, enabling consistent and controllable LOD generation for architectural models and collections.

Findings

Co-LOD produces accurate and consistent LODs across diverse architectural inputs.

The method maintains semantic integrity during LOD generation.

Experiments show superior detail and quality in LOD representations.

Abstract

Managing the level-of-detail (LOD) in architectural models is crucial yet challenging, particularly for effective representation and visualization of buildings. Traditional approaches often fail to deliver controllable detail alongside semantic consistency, especially when dealing with noisy and inconsistent inputs. We address these limitations with \emph{Co-LOD}, a new approach specifically designed for effective LOD management in architectural modeling. Co-LOD employs shape co-analysis to standardize geometric structures across multiple buildings, facilitating the progressive and consistent generation of LODs. This method allows for precise detailing in both individual models and model collections, ensuring semantic integrity. Extensive experiments demonstrate that Co-LOD effectively applies accurate LOD across a variety of architectural inputs, consistently delivering superior detail and quality in LOD representations.