| 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 |
| date | 2026-07-16 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.15058 |
| upvotes | 4 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | CAD-to-image alignment aims to estimate an object's 9D pose (rotation, translation, and anisotropic scale) from a single RGB image, enabling applications in robotics and augmented reality. Recent zero-shot methods use visual foundation models to match image regions to CAD models, yet typically their correspondences are appearance-driven and degrade under occlusion or sim-to-real domain shift. To address these limitations, we introduce SUFLECA (Scaling Up Feature LEarning for CAD 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 674K images spanning 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 one-to-one CAD-to-image correspondences. Together, these contributions enable accurate, sub-second alignment per object instance without iterative pose refinement. On ScanNet25k, SUFLECA achieves 33.4%/42.3% category/instance accuracy, outperforming, with a smaller computational footprint, the strongest zero-shot baseline by 10.3/12.2 percentage points and, for the first time on this benchmark, even surpassing fully supervised methods. Code is available at: https://github.com/snt-arg/SUFLECA |
cite
@misc{ejaz2026sufleca,
title={SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment},
author={Saad Ejaz and Miguel Fernandez-Cortizas and Javier Civera and Holger Voos and Jose Luis Sanchez-Lopez},
year={2026},
eprint={2607.15058},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.15058}
}