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Circular-symmetric correlation layer
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DOI：10.1007/s10994-022-06288-4.png)
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Abstract
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Despite the vast success of standard planar convolutional neural networks, they are not the most efficient choice for analyzing signals that lie on an arbitrarily curved manifold, such as a cylinder. The problem arises when one performs a planar projection of these signals and inevitably causes them to be distorted or broken where there is valuable information. We propose a Circular-symmetric Correlation Layer (CCL) based on the formalism of roto-translation equivariant correlation on the continuous group S-1 x R, and implement it efficiently using the well-known Fast Fourier Transform (FFT) algorithm. We showcase the performance analysis of a general network equipped with CCL on various recognition and classification tasks and datasets.
Keywords:
Equivariant neural networks
Panoramic image processing
LiDAR segmentation
Fast Fourier transform (FFT)
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