CoordAR：通过自回归坐标图生成实现新型物体的单参考6D姿态估计
CoordAR: One-Reference 6D Pose Estimation of Novel Objects via Autoregressive Coordinate Map Generation
AI总结：
CoordAR通过自回归坐标图生成方法，实现对新型物体的单参考6D姿态估计，提升了对称性和遮挡等挑战的鲁棒性。
摘要：
6D姿态估计是机器人和增强现实应用中的关键任务，当处理具有3D模型不可用的新型物体时，该任务变得尤为具有挑战性。为了减少对3D模型的依赖，最近的研究探索了基于单参考的姿态估计方法，该方法仅需要一个参考视图而不是完整的3D模型。然而，现有依赖于实值坐标回归的方法由于卷积架构的局部性质而存在全局一致性有限的问题，并且在对称或遮挡场景中面临缺乏不确定性建模的挑战。我们提出了CoordAR，一种新的自回归框架，用于未见物体的单参考6D姿态估计。CoordAR将参考视图与查询视图之间的3D-3D对应关系建模为一个离散标记的地图，该地图以自回归和概率的方式获得。为了实现准确的对应关系回归，CoordAR引入了三种新机制：1）一种新颖的坐标图标记化方法，使在离散3D空间上进行概率预测成为可能；2）一种模态解耦的编码策略，分别编码RGB外观和坐标线索；3）一个自回归变压器解码器，该解码器在位置对齐的查询特征和部分生成的标记序列上进行条件化。通过这些新机制，CoordAR在多个基准上显著优于现有方法，并在现实世界测试中表现出对称性、遮挡和其他挑战的强鲁棒性。
英文摘要：
Object 6D pose estimation, a crucial task for robotics and augmented reality applications, becomes particularly challenging when dealing with novel objects whose 3D models are not readily available. To reduce dependency on 3D models, recent studies have explored one-reference-based pose estimation, which requires only a single reference view instead of a complete 3D model. However, existing methods that rely on real-valued coordinate regression suffer from limited global consistency due to the local nature of convolutional architectures and face challenges in symmetric or occluded scenarios owing to a lack of uncertainty modeling. We present CoordAR, a novel autoregressive framework for one-reference 6D pose estimation of unseen objects. CoordAR formulates 3D-3D correspondences between the reference and query views as a map of discrete tokens, which is obtained in an autoregressive and probabilistic manner. To enable accurate correspondence regression, CoordAR introduces 1) a novel coordinate map tokenization that enables probabilistic prediction over discretized 3D space; 2) a modality-decoupled encoding strategy that separately encodes RGB appearance and coordinate cues; and 3) an autoregressive transformer decoder conditioned on both position-aligned query features and the partially generated token sequence. With these novel mechanisms, CoordAR significantly outperforms existing methods on multiple benchmarks and demonstrates strong robustness to symmetry, occlusion, and other challenges in real-world tests.
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