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Multiview Feature Aggregation for Facade Parsing
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DOI：10.1109/LGRS.2020.3035721.png)
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摘要
En 中文
Facade image parsing is essential to the semantic understanding and 3-D reconstruction of urban scenes. Considering the occlusion and appearance ambiguity in single-view images and the easy acquisition of multiple views, in this letter, we propose a multiview enhanced deep architecture for facade parsing. The highlight of this architecture is a cross-view feature aggregation module that can learn to choose and fuse useful convolutional neural network (CNN) features from nearby views to enhance the representation of a target view. Benefitting from the multiview enhanced representation, the proposed architecture can better deal with the ambiguity and occlusion issues. Moreover, our cross-view feature aggregation module can be straightforwardly integrated into existing single-image parsing frameworks. Extensive comparison experiments and ablation studies are conducted to demonstrate the good performance of the proposed method and the validity and transportability of the cross-view feature aggregation module.
Keyword:
Semantics
Image segmentation
Feature extraction
Training
Three-dimensional displays
Data models
Computer architecture
Facade parsing
feature aggregation
multiview
wide baseline
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16.4
论文数:
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