通过分步匹配提升新物体姿态估计精度
PicoPose: Progressive Pixel-to-Pixel Correspondence Learning for Novel Object Pose Estimation
- 三阶段像素级对应学习：粗匹配、全局变换平滑、局部精细优化
- 在BOP基准7个核心数据集上达到当前最佳性能
- 适合需要快速部署的新物体姿态估计场景
基于RGB的新物体姿态估计对机器人快速部署至关重要，但零样本泛化仍是关键挑战。本文提出PicoPose，一种采用三阶段像素到像素对应学习的框架。首先，将真实图像特征与渲染模板特征匹配，确定最佳模板并建立粗略对应关系；其次，通过全局回归2D仿射变换（包含平面内旋转、缩放和二维平移）平滑对应关系；最后，将仿射变换应用于最佳模板的特征图，并在局部区域内学习对应偏移，实现细粒度匹配。通过逐步精炼对应关系，PicoPose显著提升了基于PnP/RANSAC计算的姿态精度。在BOP基准的七个核心数据集上达到当前最优表现，展现出对新物体的强大泛化能力。代码与训练模型已开源。
原文摘要 · Abstract (English)
RGB-based novel object pose estimation is critical for rapid deployment in robotic applications, yet zero-shot generalization remains a key challenge. In this paper, we introduce PicoPose, a novel framework designed to tackle this task using a three-stage pixel-to-pixel correspondence learning process. Firstly, PicoPose matches features from the RGB observation with those from rendered object templates, identifying the best-matched template and establishing coarse correspondences. Secondly, PicoPose smooths the correspondences by globally regressing a 2D affine transformation, including in-plane rotation, scale, and 2D translation, from the coarse correspondence map. Thirdly, PicoPose applies the affine transformation to the feature map of the best-matched template and learns correspondence offsets within local regions to achieve fine-grained correspondences. By progressively refining the correspondences, PicoPose significantly improves the accuracy of object poses computed via PnP/RANSAC. PicoPose achieves state-of-the-art performance on the seven core datasets of the BOP benchmark, demonstrating exceptional generalization to novel objects. Code and trained models are available at https://github.com/foollh/PicoPose.
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