{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-10540590","patent":{"patent_number":"US-10540590","title":"Method for generating spatial-temporally consistent depth map sequences based on convolution neural networks","assignee":null,"inventors":[],"filing_date":"2016-12-29T00:00:00.000Z","publication_date":"2020-01-21T00:00:00.000Z","cpc_codes":["G06V","G06F","G06F","G06F","G06N","G06N","G06N","G06T","G06T","G06V","G06V","H04N","G06T","G06T"],"num_claims":4,"abstract":"A method for generating spatial-temporal consistency depth map sequences based on convolutional neural networks for 2D-3D conversion of television works includes steps of: 1) collecting a training set, wherein each training sample thereof includes a sequence of continuous RGB images, and a corresponding depth map sequence; 2) processing each image sequence in the training set with spatial-temporal consistency superpixel segmentation, and establishing a spatial similarity matrix and a temporal similarity matrix; 3) establishing the convolution neural network including a single superpixel depth regression network and a spatial-temporal consistency condition random field loss layer; 4) training the convolution neural network; and 5) recovering a depth maps of a RGB image sequence of unknown depth through forward propagation with the trained convolution neural network; which avoids that clue-based depth recovery method is greatly depended on scenario assumptions, and inter-frame discontinuity between depth maps generated by conventional neural networks."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Method for generating spatial-temporally consistent depth map sequences based on convolution neural networks","description":"A method for generating spatial-temporal consistency depth map sequences based on convolutional neural networks for 2D-3D conversion of television works includes steps of: 1) collecting a training set","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-10540590","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-10540590","citation_suggestion":"Patentable. \"Method for generating spatial-temporally consistent depth map sequences based on convolution neural networks\" (US-10540590). https://patentable.app/patents/US-10540590","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-10540590","json":"https://patentable.app/api/llm-context/US-10540590","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-31T10:38:51.244Z"}