CM2LoD3: Reconstructing LoD3 Building Models Using Semantic Conflict Maps

TL;DR

This paper introduces CM2LoD3, a novel automated method for reconstructing detailed LoD3 building models by segmenting and fusing semantic conflict maps with textured models, improving urban 3D modeling efficiency.

Contribution

The paper presents a new approach leveraging semantic conflict maps and confidence-based fusion to automate LoD3 building model reconstruction, enhancing scalability and accuracy.

Findings

Achieved 61% segmentation performance with uncertainty-aware fusion.

Demonstrated effective segmentation and reconstruction of building openings.

Validated the method's potential for scalable 3D city modeling.

Abstract

Detailed 3D building models are crucial for urban planning, digital twins, and disaster management applications. While Level of Detail 1 (LoD)1 and LoD2 building models are widely available, they lack detailed facade elements essential for advanced urban analysis. In contrast, LoD3 models address this limitation by incorporating facade elements such as windows, doors, and underpasses. However, their generation has traditionally required manual modeling, making large-scale adoption challenging. In this contribution, CM2LoD3, we present a novel method for reconstructing LoD3 building models leveraging Conflict Maps (CMs) obtained from ray-to-model-prior analysis. Unlike previous works, we concentrate on semantically segmenting real-world CMs with synthetically generated CMs from our developed Semantic Conflict Map Generator (SCMG). We also observe that additional segmentation of textured models can be fused with CMs using confidence scores to further increase segmentation performance and thus increase 3D reconstruction accuracy. Experimental results demonstrate the effectiveness of our CM2LoD3 method in segmenting and reconstructing building openings, with the 61% performance with uncertainty-aware fusion of segmented building textures. This research contributes to the advancement of automated LoD3 model reconstruction, paving the way for scalable and efficient 3D city modeling. Our project is available: https://github.com/InFraHank/CM2LoD3