Spherical Transformer: Adapting Spherical Signal to Convolutional Networks

Du, Haikuan; Cao, Hui; Cai, Shen; Yan, Junchi; Zhang, Siyu

doi:10.1007/978-3-031-18913-5_2

Cited by 1 publication

(1 citation statement)

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“…To yield semantic segmenters suitable for wide FoV images, they present a multi-source omni-supervised learning scheme with panoramic domain covered in the training via data distillation. [35] presents a novel method to transform the spherical signals to structured vectors that can be processed through standard CNNs directly.…”

Section: Panoramic Semantic Segmentationmentioning

confidence: 99%

Real-Time Semantic Segmentation of Spherical Images for Automotive Applications

Dell'Eva

Marco

Cerri

et al. 2023

Preprint

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Recent advancements in autonomous driving technology have resulted in a growing need for robust algorithms that can effectively detect, recognize, and segment objects in the surrounding environment. Semantic segmentation systems, which classify each pixel of an image into a class, have become a key component in achieving complete scene understanding, a crucial task for self-driving vehicles. This work focuses on utilizing deep learning methods to perform semantic segmentation on spherical images, a particular representation of fisheye images, characterized by a large field of view and strong distortion, which can result in performance degradation on convolutional neural networks (CNNs) trained on pinhole images. To address this issue, we propose the use of spherical kernels, which adapt their sampling locations according to the spherical projection, as an alternative to standard convolutional kernels, in real-time CNNs. We demonstrate that this approach outperforms the use of the regular convolution operation. To showcase the effectiveness of this method, we generate three spherical image datasets with varying characteristics.

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Section: Panoramic Semantic Segmentationmentioning

confidence: 99%