# Convolution kernel adaptation to calibrated fisheye Source: [Convolution kernel adaptation to calibrated fisheye]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:18:23.013Z Authors: Bruno Berenguel-Baeta, Maria Santos-Villafranca, Jesus Bermudez-Cameo, Alejandro Perez-Yus, Jose J. Guerrero Published: 2024-02-02T14:44:50.000Z ## Abstract Convolution kernels are the basic structural component of convolutional neural networks (CNNs). In the last years there has been a growing interest in fisheye cameras for many applications. However, the radially symmetric projection model of these cameras produces high distortions that affect the performance of CNNs, especially when the field of view is very large. In this work, we tackle this problem by proposing a method that leverages the calibration of cameras to deform the convolution kernel accordingly and adapt to the distortion. That way, the receptive field of the convolution is similar to standard convolutions in perspective images, allowing us to take advantage of pre-trained networks in large perspective datasets. We show how, with just a brief fine-tuning stage in a small dataset, we improve the performance of the network for the calibrated fisheye with respect to standard convolutions in depth estimation and semantic segmentation. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()