Learning Spherical Convolution for 360^° Recognition

While 360^° cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make visual recognition non-trivial. Ideally, 360^° imagery could inherit the deep convolutional neural networks (CNNs) already trained with great success on perspective projection images. However, spherical images cannot be projected to a single plane without significant distortion, and existing methods to transfer CNNs from perspective to spherical images introduce significant computational costs and/or degradations in accuracy. We propose to learn a Spherical Convolution Network (SphConv) that translates a planar CNN to the equirectangular projection of 360^° images. Given a source CNN for perspective images as input, SphConv learns to reproduce the flat filter outputs on 360^° data, sensitive to the varying distortion effects across the viewing sphere. The key benefits are 1) efficient and accurate recognition for 360^° images, and 2) the ability to leverage powerful pre-trained networks for perspective images. We further proposes two instantiation of SphConv-Spherical Kernel learns location dependent kernels on the sphere for SphConv, and Kernel Transformer Network learns a functional transformation that generates SphConv kernels from the source CNN. Among the two variants, Kernel Transformer Network has a much lower memory footprint at the cost of higher computational overhead. Validating our approach with multiple source CNNs and datasets, we show that SphConv using KTN successfully preserves the source CNN's accuracy, while offering efficiency, transferability, and scalability to typical image resolutions. We further introduce a spherical Faster R-CNN model based on SphConv and show that we can learn a spherical object detector without any object annotation in 360^° images.