论文标题
径向交叉点数图像：抗糊状的3D形状描述符
Radial Intersection Count Image: a Clutter Resistant 3D Shape Descriptor
论文作者
论文摘要
提出了一个新颖的形状描述符，用于radial相交计数图像（RICI），并显示出在未整理和更重要的是混乱的场景中显着胜过经典的自旋图像（SI）和3D形状上下文（3DSC）。计算和比较也更快。 RICI的混乱性主要是由于设计新型距离函数的设计，能够在很大程度上忽略混乱。与两个计数点样品的Si和3DSC相比，RICI使用与网格表面的交点计数，因此无噪声。为了有效的RICI结构，开发了新的普遍关注算法。其中包括有效的圆形三角形交集算法和一种用于将点投射到Si-like（$α$，$β$）坐标的算法。还引入了“杂物盒实验”，是评估描述符对混乱的响应的更好方法。在此框架中评估了SI，3DSC和RICI，并清楚地证明了RICI的优势。
A novel shape descriptor for cluttered scenes is presented, the Radial Intersection Count Image (RICI), and is shown to significantly outperform the classic Spin Image (SI) and 3D Shape Context (3DSC) in both uncluttered and, more significantly, cluttered scenes. It is also faster to compute and compare. The clutter resistance of the RICI is mainly due to the design of a novel distance function, capable of disregarding clutter to a great extent. As opposed to the SI and 3DSC, which both count point samples, the RICI uses intersection counts with the mesh surface, and is therefore noise-free. For efficient RICI construction, novel algorithms of general interest were developed. These include an efficient circle-triangle intersection algorithm and an algorithm for projecting a point into SI-like ($α$, $β$) coordinates. The 'clutterbox experiment' is also introduced as a better way of evaluating descriptors' response to clutter. The SI, 3DSC, and RICI are evaluated in this framework and the advantage of the RICI is clearly demonstrated.
