发表机构
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg; Faculty of Science, Technology, and Medicine, University of Luxembourg; I3A, Universidad de Zaragoza（自动化与机器人研究组，卢森堡大学跨学科安全、可靠性与信任中心（SnT）; 卢森堡大学科学、技术与医学学院; 萨拉戈萨大学I3A）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在解决CAD到图像对齐问题，提出弱监督框架SUFLECA。通过归一化物体坐标监督扩大特征学习，结合几何一致匹配算法，能准确快速对齐，在ScanNet25k上取得优异成绩，优于零样本基线并超越全监督方法。
AI 中文摘要
CAD到图像对齐旨在从单个RGB图像估计物体的9D姿态，用于机器人技术和增强现实等应用。近期的零样本方法存在局限性。为此，我们引入了SUFLECA这一弱监督框架，有两个关键贡献。一是通过对674K图像的归一化物体坐标监督扩大基于几何的特征学习，学习跨域的紧凑几何感知特征；二是提出几何一致匹配算法建立可靠对应关系。这使得能准确且在亚秒级完成对齐，在ScanNet25k上取得了良好成绩，优于零样本基线且首次超越全监督方法。
英文摘要
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: https://github.com/snt-arg/SUFLECA
