SUFLECA：擴展特徵學習以實現CAD到圖像的對齊

摘要

CAD到图像对齐的目标是从单张RGB图像中估计物体的9自由度姿态（旋转、平移及各向异性缩放），从而支持机器人与增强现实等应用。现有零样本方法利用视觉基础模型将图像区域与CAD模型匹配，但这类对应关系通常依赖外观驱动，在遮挡或仿真到真实领域迁移时表现不佳。为解决这些问题，我们提出SUFLECA（针对CAD对齐的扩展特征学习框架），这是一种用于零样本CAD对齐的弱监督框架，包含两项关键贡献。首先，SUFLECA通过标准化物体坐标监督，在涵盖12个真实与合成数据集的67.4万张图像上，从预训练视觉表征中扩展几何扎根特征学习，从而学习到跨领域泛化的紧凑几何感知特征。其次，我们提出一种几何一致性匹配算法，建立可靠的CAD到图像一对一对应关系。这些贡献使得每个物体实例能够实现精准、亚秒级对齐，无需迭代姿态优化。在ScanNet25k数据集上，SUFLECA达到33.4%/42.3%的分类/实例准确率，以更小的计算开销超越最强的零样本基线10.3/12.2个百分点，并且在该基准上首次超越全监督方法。代码地址：https://github.com/snt-arg/SUFLECA

English

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