由于传统的不变量方法是针对位置坐标进行计算，难以识别形状特征有微小区别的3维目标。为了能方便地识别有微小区别的3维目标，首先利用平均曲率来描述空间曲面的固有特征，并将传统的3维矩不变量和曲率思想相融合，构造出了一类新的矩不变量——空间曲率矩不变量；然后通过归一化过程，证明了这类不变量对平移、旋转和尺度变换具有无关性。实验表明，空间曲率矩不变量方法和传统的方法相比，不仅能够更好地对形状相似的目标进行分类，并能降低运算复杂度。

The three-dimensional shape descriptors have been applied to object recognition and classification. A novel three-dimensional shape descriptor combined with local and global representations is proposed in this paper. Firstly，traditional moment invariants are extended by including a term to represent spatial curvature and a series of new moment invariants named spatial curvature moment invariants is constructed. Secondly，normalization method of these invariants is presented and they are independent of the translation，rotation and scaling transforms. Experiments indicate the proposed method is of lower computation complexity than traditional three-dimensional shape descriptors without reducing the recognition rate.