Holistic Multi-View Building Analysis in the Wild with Projection Pooling

• URL: http://arxiv.org/abs/2008.10041v3
• Date: Sat, 19 Dec 2020 20:59:07 GMT
• Title: Holistic Multi-View Building Analysis in the Wild with Projection Pooling
• Authors: Zbigniew Wojna, Krzysztof Maziarz, {\L}ukasz Jocz, Robert Pa{\l}uba, Robert Kozikowski, Iasonas Kokkinos
• Abstract summary: We address six different classification tasks related to fine-grained building attributes. Tackling such a remote building analysis problem became possible only recently due to growing large-scale datasets of urban scenes. We introduce a new benchmarking dataset, consisting of 49426 images (top-view and street-view) of 9674 buildings.
• Score: 18.93067906200084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address six different classification tasks related to fine-grained building attributes: construction type, number of floors, pitch and geometry of the roof, facade material, and occupancy class. Tackling such a remote building analysis problem became possible only recently due to growing large-scale datasets of urban scenes. To this end, we introduce a new benchmarking dataset, consisting of 49426 images (top-view and street-view) of 9674 buildings. These photos are further assembled, together with the geometric metadata. The dataset showcases various real-world challenges, such as occlusions, blur, partially visible objects, and a broad spectrum of buildings. We propose a new projection pooling layer, creating a unified, top-view representation of the top-view and the side views in a high-dimensional space. It allows us to utilize the building and imagery metadata seamlessly. Introducing this layer improves classification accuracy -- compared to highly tuned baseline models -- indicating its suitability for building analysis.

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