基于三维数据分析与视觉基础模型二维投影的三维点云数据损伤分类
Damage Classification for 3D Point Cloud Data via 3D Data Analysis and Vision Foundation Model-based 2D Projections
- City College of New York（纽约城市学院）
- Air Force Research Lab（空军研究实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对三维点云损伤分类的挑战，提出3PDA与2PDA两种算法，2PDA准确率略低但计算成本大幅降低且泛化性更强，为相关任务提供了高效替代方案。
AI中文摘要：
三维点云数据（PCD）的细粒度损伤分类仍是一项长期存在的挑战，受限于高计算需求和有限的标注数据。本研究考察两种方法：基于三维PCD的损伤评估（3PDA）算法和基于二维投影的损伤评估（2PDA）算法。在我们的3PDA分析算法中，使用拓扑数据分析（TDA）从PointNet分割的三维PCD中导出紧凑表示，随后将其与异常检测算法结合以量化结构退化。我们表明，TDA可有效将视觉基础模型（VFM）分割组件的几何结构压缩为判别特征向量，且异常检测模型仅使用三维PCD输入即可可靠区分具有不同损伤严重程度的组件。在2D投影分析算法中，我们利用大型VFM，通过将三维PCD投影到二维视图来进行细粒度损伤检测。这些投影使基于VFM的模型能够达到有竞争力的分类性能，同时仅需处理完整三维数据相关计算成本的一小部分。我们的结果表明，2PDA中的二维VFM管道在细粒度损伤分类任务上表现强劲，凸显其作为传统3PDA架构的轻量、资源高效替代方案的可行性。对比评估显示，3PDA在较窄的对象几何子集上实现了更高的准确率，但由于依赖TDA和高保真三维数据集的稀缺性，其计算成本显著更高；相比之下，2PDA算法的准确率略低，但时间复杂度降低了一个数量级，且能在更广泛的对象类别上实现泛化。
英文摘要：
Fine-grained damage classification of 3D point cloud data (PCD) remains a persistent challenge, constrained by high computational demands and limited labeled data. This study examines two methods: 3D PCD-based damage assessment (3PDA) algorithm and 2D projection damage assessment (2PDA) In our 3PDA analysis algorithm, TDA is used to derive compact representations of 3D PCD segmented by pointNet, which are then integrated with anomaly detection algorithms to quantify structural degradation. We show that TDA effectively compresses geometric structure from VFM-segmented components into discriminative feature vectors and that anomaly detection models can reliably distinguish components with varying damage severity using only 3D PCD inputs. In the 2D projection analysis algorithm, we leverage large VFMs for granular damage detection by projecting 3D PCD into 2D views. These projections allow VFM based models to achieve competitive classification performance while requiring only a fraction of the computational cost associated with full 3D data processing. Our results demonstrate that 2D VFM pipelines in 2PDA can perform strongly on fine-grained damage classification tasks, highlighting their viability as lightweight, resource-efficient alternatives to traditional 3PDA architectures. Comparative evaluation shows that the 3PDA attains higher accuracy but only for a narrow subset of object geometries and at substantially higher computational cost due to its reliance on TDA and the scarcity of high-fidelity 3D datasets. In contrast, the 2PDA algorithm yields slightly lower accuracy but offers an order of magnitude reduction in time complexity and generalizes across a far broader range of object categories.
