Abstract: 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 $S^1 \times \mathbb{R}$, 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 a popular autonomous driving dataset, nuScenes (Caesar et al., 2020), for semantic segmentation of 3D point clouds obtained from LiDAR sweeps from their $360^\circ-$panoramic projections. The PyTorch package implementation of CCL is provided online.