{"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/gs-blur-a-3d-scene-based-dataset-for","title":"GS-Blur: A 3D Scene-Based Dataset for Realistic Image Deblurring","arxiv_id":"2410.23658","date":"2024-10-31","proceeding":null,"authors":["Dongwoo Lee","JoonKyu Park","Kyoung Mu Lee"],"abstract":"To train a deblurring network, an appropriate dataset with paired blurry and sharp images is essential. Existing datasets collect blurry images either synthetically by aggregating consecutive sharp frames or using sophisticated camera systems to capture real blur. However, these methods offer limited diversity in blur types (blur trajectories) or require extensive human effort to reconstruct large-scale datasets, failing to fully reflect real-world blur scenarios. To address this, we propose GS-Blur, a dataset of synthesized realistic blurry images created using a novel approach. To this end, we first reconstruct 3D scenes from multi-view images using 3D Gaussian Splatting (3DGS), then render blurry images by moving the camera view along the randomly generated motion trajectories. By adopting various camera trajectories in reconstructing our GS-Blur, our dataset contains realistic and diverse types of blur, offering a large-scale dataset that generalizes well to real-world blur. Using GS-Blur with various deblurring methods, we demonstrate its ability to generalize effectively compared to previous synthetic or real blur datasets, showing significant improvements in deblurring performance.","url_abs":"https://arxiv.org/abs/2410.23658v1","url_pdf":"https://arxiv.org/pdf/2410.23658v1.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":"gs-blur-a-3d-scene-based-dataset-for","repo_url":"https://github.com/dongwoohhh/GS-Blur","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"3dgs","task_name":"3DGS"},{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"image-deblurring","task_name":"Image Deblurring"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2410.23658","atlas_url":"https://app.syntology.ai/?focus=2410.23658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.23658"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/dongwoohhh/GS-Blur","reach":{"status":"ok","spdx":"NOASSERTION"}}],"summary":{"ran_honours":1,"ran":3,"unverified":2},"by_repo_kind":{"official":{"samples":6,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":6,"samples":[{"code_sha256_prefix":"c56b7ef16f309a45","entry":"gaussian","repo":"dongwoohhh/GS-Blur","repo_kind":"official","path":"utils/loss_utils.py","file_url":"https://github.com/dongwoohhh/GS-Blur/blob/HEAD/utils/loss_utils.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"c56b7ef16f309a45"}},{"code_sha256_prefix":"ac0e42d6fbcfbbe6","entry":"l1_loss","repo":"dongwoohhh/GS-Blur","repo_kind":"official","path":"utils/loss_utils.py","file_url":"https://github.com/dongwoohhh/GS-Blur/blob/HEAD/utils/loss_utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ac0e42d6fbcfbbe6"}},{"code_sha256_prefix":"8c3b0f873ba11813","entry":"l2_loss","repo":"dongwoohhh/GS-Blur","repo_kind":"official","path":"utils/loss_utils.py","file_url":"https://github.com/dongwoohhh/GS-Blur/blob/HEAD/utils/loss_utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8c3b0f873ba11813"}},{"code_sha256_prefix":"cdd00787894554b5","entry":"readImages","repo":"dongwoohhh/GS-Blur","repo_kind":"official","path":"metrics.py","file_url":"https://github.com/dongwoohhh/GS-Blur/blob/HEAD/metrics.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cdd00787894554b5"}},{"code_sha256_prefix":"aea407a211053656","entry":"compute_avg_distance_cams","repo":"dongwoohhh/GS-Blur","repo_kind":"official","path":"utils/blur_utils.py","file_url":"https://github.com/dongwoohhh/GS-Blur/blob/HEAD/utils/blur_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"aea407a211053656"}},{"code_sha256_prefix":"82f66f6331f638fd","entry":"compute_bezier_coeff","repo":"dongwoohhh/GS-Blur","repo_kind":"official","path":"utils/blur_utils.py","file_url":"https://github.com/dongwoohhh/GS-Blur/blob/HEAD/utils/blur_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"82f66f6331f638fd"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}