4,442 research outputs found
Holistic Multi-View Building Analysis in the Wild with Projection Pooling
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.Comment: Accepted for publication at the 35th AAAI Conference on Artificial
Intelligence (AAAI 2021
Holistic Multi-View Building Analysis in the Wild with Projection Pooling
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 \emph{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
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
In this paper, we present a method to utilize 2D-2D point matches between
images taken during different image conditions to train a convolutional neural
network for semantic segmentation. Enforcing label consistency across the
matches makes the final segmentation algorithm robust to seasonal changes. We
describe how these 2D-2D matches can be generated with little human interaction
by geometrically matching points from 3D models built from images. Two
cross-season correspondence datasets are created providing 2D-2D matches across
seasonal changes as well as from day to night. The datasets are made publicly
available to facilitate further research. We show that adding the
correspondences as extra supervision during training improves the segmentation
performance of the convolutional neural network, making it more robust to
seasonal changes and weather conditions.Comment: In Proc. CVPR 201
- âŠ