{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spherephd-applying-cnns-on-a-spherical","title":"SpherePHD: Applying CNNs on a Spherical PolyHeDron Representation of 360 degree Images","arxiv_id":"1811.08196","date":"2018-11-20","proceeding":null,"authors":["Yeonkun Lee","Jaeseok Jeong","Jongseob Yun","Wonjune Cho","Kuk-Jin Yoon"],"abstract":"Omni-directional cameras have many advantages overconventional cameras in\nthat they have a much wider field-of-view (FOV). Accordingly, several\napproaches have beenproposed recently to apply convolutional neural\nnetworks(CNNs) to omni-directional images for various visual tasks.However,\nmost of them use image representations defined inthe Euclidean space after\ntransforming the omni-directionalviews originally formed in the non-Euclidean\nspace. Thistransformation leads to shape distortion due to nonuniformspatial\nresolving power and the loss of continuity. Theseeffects make existing\nconvolution kernels experience diffi-culties in extracting meaningful\ninformation.This paper presents a novel method to resolve such prob-lems of\napplying CNNs to omni-directional images. Theproposed method utilizes a\nspherical polyhedron to rep-resent omni-directional views. This method\nminimizes thevariance of the spatial resolving power on the sphere sur-face,\nand includes new convolution and pooling methodsfor the proposed\nrepresentation. The proposed method canalso be adopted by any existing\nCNN-based methods. Thefeasibility of the proposed method is demonstrated\nthroughclassification, detection, and semantic segmentation taskswith synthetic\nand real datasets.","url_abs":"http://arxiv.org/abs/1811.08196v2","url_pdf":"http://arxiv.org/pdf/1811.08196v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spherephd-applying-cnns-on-a-spherical","repo_url":"https://github.com/KAIST-vilab/SpherPHD_public","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"spherephd-applying-cnns-on-a-spherical","repo_url":"https://github.com/keevin60907/SpherePHD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.08196","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}