IEEE Trans Cybern. 2014 Dec;44(12):2379-90. doi: 10.1109/TCYB.2014.2307121.
The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios.
在廉价的多传感器设备（如 Kinect）基于结构光出现之后，深度图的使用变得越来越有吸引力。在这种情况下，人们强烈需要能够生成丰富对象表示的强大的 3-D 形状描述符。尽管已经在文献中提出了几种 3-D 描述符，但具有判别力和计算效率的描述符的研究仍然是一个开放的问题。在本文中，我们提出了一种新的点云描述符，称为球形模糊形状模型（SBSM），它基于形状体素距离和邻域传播策略成功地编码了物体的结构密度和局部变化。所提出的 SBSM 被证明是旋转和尺度不变的，对噪声和遮挡具有鲁棒性，对于复杂物体（如人手）的多个类别具有高度的判别力，并且由于 SBSM 的复杂度与物体体素的数量呈线性关系，因此计算效率很高。在公共深度多类对象数据、3-D 面部表情数据和新的手姿势数据集上的实验评估表明，与最先进的方法相比，性能有了显著提高。此外，该提案还证明了其在 3-D 场景中的对象定位和人机交互场景中的实时自动手姿势识别中的有效性。
