发表机构
Texas A&M University（德克萨斯A&M大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GUARD提出几何不确定性感知框架，结合多尺度几何Transformer和高斯过程估计逐点可靠性，在单次前向中实现点云去噪与分割，显著提升HDD拆解中的分割精度和伪影检测能力。
AI 中文摘要
可靠的机器人拆解需要部件级表示，以区分真实部件几何与扫描和重建伪影。在硬盘驱动器（HDD）的点云中，结构化鬼影伪影在局部可能类似于有效部件，但与整体几何不一致，导致错误的测量结果获得看似合理的语义标签。这构成了一个工程信息问题：语义预测置信度本身并不能确定底层几何是否可靠。我们提出GUARD，一种几何不确定性感知框架，通过建模学习到的几何表示的可靠性，在单次前向传播中执行点过滤和分割。GUARD结合了多尺度几何Transformer与多带宽随机傅里叶特征高斯过程来估计逐点几何不确定性，并辅以预测熵来抑制不可靠的测量，同时保留信息丰富的结构。在2,745个真实HDD点云上的评估表明，GUARD将PointNet++分割的平均交并比从0.7739提高到0.8318。在ShapeNetPart和ScanNet上的额外实验考察了在不同损坏类型、点云域和分割骨干网络下的鲁棒性。在手动标注的ScanNet样本上，几何不确定性实现了0.7931的损坏点检测F1分数，而预测熵为0.2212。结果证明了区分几何可靠性与语义置信度的价值，并揭示了伪影抑制与信息结构保留之间的权衡。GUARD为解释不完美的3D测量以进行部件识别和后续机器人操作提供了一种可靠性感知方法。项目网站：this https URL。
英文摘要
Reliable robotic disassembly requires part-level representations that distinguish genuine component geometry from scanning and reconstruction artifacts. In point clouds of hard disk drives (HDDs), structured ghost artifacts can resemble valid components locally while remaining inconsistent with the overall geometry, allowing erroneous measurements to receive plausible semantic labels. This creates an engineering information problem: semantic prediction confidence alone does not establish whether the underlying geometry is reliable. We propose \textbf{GUARD}, a geometric uncertainty-aware framework that performs point filtering and segmentation within a single forward pass by modeling the reliability of learned geometric representations. GUARD combines a multi-scale geometric transformer with a multi-bandwidth random Fourier feature Gaussian Process to estimate per-point geometric uncertainty, complemented by predictive entropy to suppress unreliable measurements while preserving informative structures. Evaluation on 2,745 real HDD point clouds shows that GUARD improves PointNet++ segmentation mean intersection over union from 0.7739 to 0.8318. Additional experiments on ShapeNetPart and ScanNet examine robustness across corruption types, point-cloud domains, and segmentation backbones. On manually annotated ScanNet samples, geometric uncertainty achieves a corrupted-point detection F1 score of 0.7931, compared with 0.2212 for predictive entropy. The results demonstrate the value of distinguishing geometric reliability from semantic confidence and reveal a tradeoff between artifact suppression and preservation of informative structures. GUARD contributes a reliability-aware approach to interpreting imperfect 3D measurements for component identification and subsequent robotic handling. Project website: https://001-wang.github.io/GUARD_Point_denoiser/.
