Optimized Vectorizing of Building Structures with Switch: High-Efficiency Convolutional Channel-Switch Hybridization Strategy
arxiv(2023)
摘要
The building planar graph reconstruction, a.k.a. footprint reconstruction,
which lies in the domain of computer vision and geoinformatics, has been long
afflicted with the challenge of redundant parameters in conventional
convolutional models. Therefore, in this letter, we proposed an advanced and
adaptive shift architecture, namely the Switch operator, which incorporates
non-exponential growth parameters while retaining analogous functionalities to
integrate local feature spatial information, resembling a high-dimensional
convolution operation. The Switch operator, cross-channel operation,
architecture implements the XOR operation to alternately exchange adjacent or
diagonal features, and then blends alternating channels through a 1x1
convolution operation to consolidate information from different channels. The
SwitchNN architecture, on the other hand, incorporates a group-based
parameter-sharing mechanism inspired by the convolutional neural network
process and thereby significantly reducing the number of parameters. We
validated our proposed approach through experiments on the SpaceNet corpus, a
publicly available dataset annotated with 2,001 buildings across the cities of
Los Angeles, Las Vegas, and Paris. Our results demonstrate the effectiveness of
this innovative architecture in building planar graph reconstruction from 2D
building images.
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