SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment

[2607.15058] SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment Skip to main content Search Submit Donate Log in Computer Science > Computer Vision and Pattern Recognition arXiv:2607.15058 (cs) [Submitted on 16 Jul 2026 ( v1 ), last revised 28 Aug 2026 (this version, v2)] Title: SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment Authors: Saad Ejaz , Miguel Fernandez-Cortizas , Javier Civera , Holger Voos , Jose Luis Sanchez-Lopez View PDF HTML (experimental) Abstract: CAD-to-image alignment aims to estimate an object's 9D pose (rotation, translation, and anisotropic scale) from a single RGB image, with applications in robotics and augmented reality. Recent zero-shot methods use vision foundation models to match image regions to CAD models; yet their correspondences are typically appearance-driven or unreliable under occlusion or synthetic-to-real domain shift. To address these limitations, we introduce SUFLECA (Scaling Up Feature LEarning for CAD-to-image Alignment), a weakly supervised framework for zero-shot CAD alignment with two key contributions. First, SUFLECA scales up geometry-grounded feature learning from pretrained visual representations through Normalized Object Coordinates (NOCs) supervision on images spanning up to 12 real and synthetic datasets, learning compact geometry-aware features that generalize across domains. Second, we propose a geometrically consistent matching algorithm that establishes reliable CAD-to-image correspondences. Together, these contributions enable accurate, sub-second alignment per object instance without iterative pose refinement. On ScanNet25k, SUFLECA achieves 32.8%/42.6% category/instance accuracy, outperforming the strongest zero-shot baseline by 9.7/12.5 percentage points with a smaller computational footprint, and for the first time on this benchmark, even surpassing existing pose-supervised methods. Code is available at: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Robotics (cs.RO) Cite as: arXiv:2607.15058 [cs.CV] (or arXiv:2607.15058v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.15058 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Saad Ejaz [ view email ] [v1] Thu, 16 Jul 2026 14:32:34 UTC (6,677 KB) [v2] Fri, 28 Aug 2026 07:51:16 UTC (23,613 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CV < prev | next > new | recent | 2026-07 Change to browse by: cs cs.RO References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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