AURORA：用于手内重建的主动不确定性驱动重定向
AURORA: Active Uncertainty-Driven Re-Orientation for In-Hand Reconstruction
AI总结：
AURORA提出主动不确定性驱动的重定向框架，通过Ray-GPIS估计方向性不确定性并选择最佳视图，结合手内旋转策略，提升手内物体重建质量与效率。
AI中文摘要：
观察被机器人手抓取的物体因严重的视觉遮挡而具有挑战性。尽管手内操作可以暴露隐藏表面，但现有方法通常依赖预定义或开环的重定向策略，这些策略并未明确针对观察不足的区域。我们提出AURORA，一个主动3D重建框架，它闭环了在线物体中心重建与手内重定向。其核心是Ray-GPIS，它沿候选观察射线估计方向性重建不确定性，并使用不确定性-新颖性目标选择下一个最佳视图目标，这些通过轴条件的手内旋转策略实现。所得的RGB-D观测通过无CAD的6D姿态跟踪和轻量级几何重建增量融合。实验表明，AURORA在重建质量和信息获取效率上优于非主动旋转策略，而Ray-GPIS在重建性能、动作排序质量和规划效率上也优于主动视图规划基线。针对性的消融研究进一步验证了其对手部遮挡和姿态误差的鲁棒性。项目网页可在以下网址访问：此https URL。
英文摘要：
Observing objects grasped by a robot hand is challenging due to severe visual occlusions. Although in-hand manipulation can expose hidden surfaces, existing approaches often rely on predefined or open-loop reorientation strategies that do not explicitly target under-observed regions. We propose AURORA, an active 3D reconstruction framework that closes the loop between online object-centric reconstruction and in-hand reorientation. At its core, Ray-GPIS estimates direction-wise reconstruction uncertainty along candidate viewing rays and selects next-best-view targets using an uncertainty--novelty objective, which are realized through an axis-conditioned in-hand rotation policy. The resulting RGB-D observations are fused incrementally using CAD-free 6D pose tracking and lightweight geometric reconstruction. Experiments demonstrate that AURORA improves reconstruction quality and information-acquisition efficiency over non-active rotation strategies, while Ray-GPIS also outperforms active view-planning baselines in reconstruction performance, action-ranking quality, and planning efficiency. Targeted ablations further validate its robustness to hand occlusion and pose errors. The project webpage is available at https://aurorahand.github.io/
