仅用一张未对齐的RGB-D图像，实现未知物体的精准位姿估计。
UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image
- 构建SE(3)不变参考系，统一不同位姿和尺度下的物体表示
- 在BOP基准上优于传统与学习方法，接近基于CAD模型的方法表现
- 适合无标定、无多视角参考的快速部署场景
现有未知物体位姿估计方法通常依赖于CAD模型或多视角参考图像，导致部署成本高。为简化参考获取，本文提出仅需单张未对齐的RGB-D参考图像即可完成位姿估计的新方法——UNOPose。由于参考图像未对齐，相对位姿可能覆盖整个SE(3)空间，且受遮挡、传感器噪声和极端几何影响，视图重叠度低，带来巨大挑战。为此，我们设计了一种粗到精框架，构建SE(3)不变参考系以标准化物体表示；针对视图重叠小的问题，基于对应点属于重叠区域的概率动态调整权重。在基于BOP Challenge构建的基准上评估显示，UNOPose在单参考设置下显著超越传统及学习方法，性能接近基于CAD模型的方法。代码与数据集已开源。
原文摘要 · Abstract (English)
Unseen object pose estimation methods often rely on CAD models or multiple reference views, making the onboarding stage costly. To simplify reference acquisition, we aim to estimate the unseen object's pose through a single unposed RGB-D reference image. While previous works leverage reference images as pose anchors to limit the range of relative pose, our scenario presents significant challenges since the relative transformation could vary across the entire SE(3) space. Moreover, factors like occlusion, sensor noise, and extreme geometry could result in low viewpoint overlap. To address these challenges, we present a novel approach and benchmark, termed UNOPose, for unseen one-reference-based object pose estimation. Building upon a coarse-to-fine paradigm, UNOPose constructs an SE(3)-invariant reference frame to standardize object representation despite pose and size variations. To alleviate small overlap across viewpoints, we recalibrate the weight of each correspondence based on its predicted likelihood of being within the overlapping region. Evaluated on our proposed benchmark based on the BOP Challenge, UNOPose demonstrates superior performance, significantly outperforming traditional and learning-based methods in the one-reference setting and remaining competitive with CAD-model-based methods. The code and dataset are available at https://github.com/shanice-l/UNOPose.
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