用相对角度替代法向量，大幅降低3D混凝土缺陷分割的存储需求。
A Storage-Efficient Feature for 3D Concrete Defect Segmentation to Replace Normal Vector
- 提出相对角度特征，仅用单维数值表征表面方向信息。
- 相比法向量，存储减少27.6%，输入通道压缩83%，性能相当。
- 适合在资源受限设备上运行，无需修改模型结构。
点云损伤重建为克服图像方法受背景噪声影响提供了有效方案，但其应用受限于3D数据量大。本文提出一种新特征——相对角度，即某点法向量与父点云平均法向量之间的夹角。该单维特征可提供与法向量等效的方向性信息，用于表征混凝土表面缺陷。基于熵的特征评估表明，相对角度能有效剔除无损区域的冗余信息，同时保留损伤区域的有效特征。使用PointNet++训练测试发现，基于相对角度的模型性能与法向量模型相当，却实现27.6%的存储降低和83%的输入通道压缩。该特征有望在不修改模型架构的前提下，支持资源受限硬件上的大规模批量推理。
原文摘要 · Abstract (English)
Point cloud reconstruction of damage offers an effective solution to image-based methods vulnerable to background noise, yet its application is constrained by the high volume of 3D data. This study proposes a new feature, relative angle, computed as the angle between the normal vector of a point and the average normal vector of its parent point cloud. This single-dimensional feature provides directionality information equivalent to normal vectors for concrete surface defect characteristics. Through entropy-based feature evaluation, this study demonstrates the ability of relative angle to filter out redundant information in undamaged sections while retaining effective information in damaged sections. By training and testing with PointNet++, models based on the relative angles achieved similar performance to that of models based on normal vectors while delivering 27.6% storage reduction and 83% input channel compression. This novel feature has the potential to enable larger-batch execution on resource-constrained hardware without the necessity of architectural modifications to models.
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