arXiv Computer Vision
Sep 1

SVI2LoD3: Agent-Driven Reconstruction of LoD3 Facade Openings in Semantic 3D City Models from Volunteered Street View Imagery using Large Language and Visual Models

arXiv:2608.29992v1 Announce Type: new Abstract: This paper presents an end-to-end, agent-driven pipeline for the LoD3 reconstruction of facade openings in 3D city models, producing directly usable Ci...

By Elmehdi Kanna, Lukas Arzoumanidis, Huynh Duc An Son Nguyen, Youness Dehbi
arXiv Machine Learning
Aug 28

A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation

The paper introduces a framework that adapts a diffusion model to a target urban domain using only imperfect pseudo‑labels, enabling the generation of high‑fidelity, target‑aligned images from semantic maps of any synthetic dataset. By filtering poor generations, correcting image‑label misalignments, and standardising semantics, the method transforms low‑effort synthetic data into competitive real‑domain training sets. Experiments on five synthetic and two real datasets show up to +8.0 %pt mIoU improvement over state‑of‑the‑art translation methods, demonstrating that rapidly constructed synthetic datasets can match the performance of high‑effort, manually designed ones.

By Damjan Kal\v{s}an, Denis Zavadski, Tim K\"uchler, Haebom Lee, Stefan Roth, Carsten Rother
Hugging Face Trending Papers
Aug 17

Remote-Sensing City Layout Extraction with MLLM

Remote-sensing systems usually describe urban content with detection boxes, semantic masks, or vector boundaries. Such outputs locate classes and support image-plane scoring, yet they do not by themselves constitute an executable layout that retains object identities, typed relations, topology, and regeneration rules.

arXiv Machine Learning
Oct 19

Towards Reconstructing Geographically Diverse Architecture with 3D Foundation Models

The paper introduces ArchWorld, a benchmark of 3D architectural landmarks with extensive geographic coverage and detailed metadata, to evaluate and analyze the performance of 3D foundation models (3DFMs). Through a comprehensive error analysis, the authors uncover geographic disparities in model accuracy that stem from variations in data representation, scene size, and region-specific architectural features. They propose an intervention scheme that enhances 3DFM performance while simultaneously reducing these geographic disparities.

By Nicky Kriplani, Yiwen Zhang, Angelina Wang, Hadar Averbuch-Elor