Adaptive Receptive Field Convolution for Top-view Fisheye Images Segmentation

Abstract

Top-view fisheye cameras are cost-efficient devices used for omnidirectional perception. However, the wide field of view (FOV) of these cameras causes significant image distortion, while the top-view setup introduces rotational symmetry, resulting in the degradation of performance of the standard convolution neural network when processing fisheye images. To address this problem, we present ARFC, a novel method called adaptive receptive field convolution, specifically designed to extract rotation- and scale-equivariant representations from top-view fisheye images. Unlike traditional orientation-static convolutions, ARFC incorporates an adaptive rotating kernel (ARK) to separate rotation from distortion and capture rotational equivariant features. Additionally, a multi-scale fusion module (MSFM) is implemented to combine scale-distorted features. Experimental evaluations conducted on THEODORE for segmentation tasks illustrate the superior performance of ARFC compared to the current state-of-the-art methods. Code is available at: https://github.com/LinMenwill/ARFC.git.