外观
论文arXiv 2609.084931 个月前
AURORA：用于手内重建的主动不确定性驱动重定向
原文链接：AURORA: Active Uncertainty-Driven Re-Orientation for In-Hand Reconstruction（arXiv 2609.08493 · 发布日期：2026-09-08）
AURORA: Active Uncertainty-Driven Re-Orientation for In-Hand Reconstruction
AURORA用Ray-GPIS将在线物体重建与手内重定向闭环，在手内三维重建实验中提升质量和信息获取效率。
核心要点
- 机器人手抓握物体时遮挡严重，预设或开环重定向难以针对欠观测区域。
- Ray-GPIS沿候选视线估计方向不确定性，并以不确定性—新颖性目标选择下一最佳视角。
- 系统通过轴条件手内旋转策略实现视角目标，并增量融合RGB-D观测进行重建。
- 实验显示AURORA优于非主动旋转策略，Ray-GPIS也优于主动视角规划基线；项目网页已公开。
验证范围与局限：验证范围为手内物体重建实验，摘要未说明是在仿真还是真机上进行。
英文摘要 (Abstract)
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/
作者：Feiyu Zhao, Yuetong Li, Chenxi Xiao
来源
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