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[bibtex]@InProceedings{Zhang_2021_ICCV, author = {Zhang, Fuyang and Xu, Xiang and Nauata, Nelson and Furukawa, Yasutaka}, title = {Structured Outdoor Architecture Reconstruction by Exploration and Classification}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {12427-12435} }
Structured Outdoor Architecture Reconstruction by Exploration and Classification
Abstract
This paper presents an explore-and-classify framework for structured architectural reconstruction from aerial image. Starting from a potentially imperfect building reconstruction by an existing algorithm, our approach 1) explores the space of building models by modifying the reconstruction via heuristic actions; 2) learns to classify the correctness of building models while generating classification labels based on the ground-truth; and 3) repeat. At test time, we iterate exploration and classification, seeking for a result with the best classification score. We evaluate the approach using initial reconstructions by two baselines and two state-of-the-art reconstruction algorithms. Qualitative and quantitative evaluations demonstrate that our approach consistently improves the reconstruction quality from every initial reconstruction.
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