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However, optimizing 3DGS requires high\u2010quality images from various viewpoints with accurate camera poses. The repeated collection of such data demands significant human effort, which poses a major constraint in practical applications. To address this issue, automated capturing systems that uses a turntable and fixed camera are widely employed. In a turntable setup, the background remains stationary while the object rotates. Therefore, preprocessing to remove the backgrond is essential, but the preprocessing reduces the number of reliable feature matches, which destabilizes Structure\u2010from\u2010Motion (SfM). This results in inaccurate camera poses, which degrades the quality of 3DGS reconstruction. We propose a novel method to optimize 3DGS in a turntable setup without SfM by leveraging the prior knowledge that objects rotate around a central axis. Unlike previous SfM\u2010free methods that estimate camera poses for each frame, our approach reduces the complexity of optimization by representing rotations with a single global rotation axis. The estimated rotation is directly applied to the 3D Gaussians, producing motion defined as rotation flow. This rotation flow is then aligned with optical flow to provide strong geometric supervision. Through uncertainty\u2010to\u2010detail flow scheduling, our approach remains stable during the initial training stage when the geometry of the Gaussian set is still inaccurate. On the NeRF\u2010Synthetic dataset and on real\u2010world datasets captured with a turntable, our method outperforms existing SfM\u2010free approaches in both reconstruction quality and training speed, and even demonstrates performance comparable to 3DGS optimized with precise camera poses.<\/jats:p>","DOI":"10.1111\/cgf.70317","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T10:53:30Z","timestamp":1775645610000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RotGS: Rotation\u2010Guided 3D Gaussian Splatting for Turntable Sequences without Structure\u2010from\u2010Motion"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9808-9022","authenticated-orcid":false,"given":"Kyumin","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Intelligence Convergence Yonsei University  Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4593-6614","authenticated-orcid":false,"given":"Dohae","family":"Lee","sequence":"additional","affiliation":[{"name":"MeTown Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8154-1969","authenticated-orcid":false,"given":"Hanul","family":"Baek","sequence":"additional","affiliation":[{"name":"Department of Computer Science Yonsei University  Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1534-1882","authenticated-orcid":false,"given":"In\u2010Kwon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science Yonsei University  Korea"},{"name":"MeTown Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"issue":"10","key":"e_1_2_8_2_2","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1145\/2001269.2001293","article-title":"Building rome in a day","volume":"54","author":"Agarwal Sameer","year":"2011","journal-title":"Communications of the ACM"},{"key":"e_1_2_8_3_2","doi-asserted-by":"crossref","unstructured":"Bian Wenjing Wang Zirui Li Kejie et al. \u201cNope-nerf: Optimising neural radiance field with no pose prior\u201d.Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.2023 4160\u201341693 7.","DOI":"10.1109\/CVPR52729.2023.00405"},{"key":"e_1_2_8_4_2","first-page":"264","volume-title":"European Conference on Computer Vision","author":"Chng Shin-Fang","year":"2022"},{"key":"e_1_2_8_5_2","doi-asserted-by":"crossref","unstructured":"Cui ZhaopengandTan Ping. \u201cGlobal structure-from-motion by similarity averaging\u201d.Proceedings of the IEEE international conference on computer vision.2015 864\u20138723.","DOI":"10.1109\/ICCV.2015.105"},{"key":"e_1_2_8_6_2","doi-asserted-by":"crossref","unstructured":"DeTone Daniel Malisiewicz Tomasz andRabinovich Andrew. \u201cSuperpoint: Self-supervised interest point detection and description\u201d.Proceedings of the IEEE conference on computer vision and pattern recognition workshops.2018 224\u20132363.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"e_1_2_8_7_2","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1109\/ICCIS.2004.1460775","volume-title":"IEEE Conference on Cybernetics and Intelligent Systems, 2004","author":"Fremont Vincent","year":"2004"},{"key":"e_1_2_8_8_2","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/3-540-49437-5_11","volume-title":"European Workshop on 3D Structure from Multiple Images of Large-Scale Environments","author":"Fitzgibbon Andrew W","year":"1998"},{"issue":"10","key":"e_1_2_8_9_2","doi-asserted-by":"crossref","first-page":"4774","DOI":"10.1109\/TIP.2019.2909640","article-title":"A performance evaluation of local features for image-based 3D reconstruction","volume":"28","author":"Fan Bin","year":"2019","journal-title":"IEEE Transactions on Image Processing"},{"key":"e_1_2_8_10_2","doi-asserted-by":"crossref","unstructured":"Fu Yang Liu Sifei Kulkarni Amey et al. \u201cColmap-free 3d gaussian splatting\u201d.Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.2024 20796\u2013208052 3 7.","DOI":"10.1109\/CVPR52733.2024.01965"},{"key":"e_1_2_8_11_2","unstructured":"Fan Jiahui Luan Fujun Yang Jian et al. \u201cFree Your Hands: Lightweight Turntable-Based Object Capture Pipeline\u201d.arXiv preprint arXiv:2503.05511(2025) 2 3 10."},{"key":"e_1_2_8_12_2","unstructured":"Gatis Daniel.rembg: Rembg is a tool to remove images background. 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