点云特征描述简介

1.特征描述子（feature descriptor）
1.1为什么要定义描述子
In their native representation, points as defined in the concept of 3D mapping systems are simply represented using their Cartesian coordinates x, y, z, with respect to a given origin. Assuming that the origin of the coordinate system does not change over time, there could be two points p1 and p2 , acquired at t1 and t2 , having the same coordinates. Comparing these points however is an ill-posed problem, because even though they are equal with respect to some distance measure (e.g. Euclidean metric), they could be sampled on completely different surfaces, and thus represent totally different information when taken together with the other surrounding points in their vicinity. That is because there are no guarantees that the world has not changed between t1 and t2. Some acquisition devices might provide extra information for a sampled point, such as an intensity or surface remission value, or even a color, however that does not solve the problem completely and the comparison remains ambiguous.
Applications which need to compare points for various reasons require better characteristics and metrics to be able to distinguish between geometric surfaces. The concept of a 3D point as a singular entity with Cartesian coordinates therefore disappears, and a new concept, that of local descriptor takes its place. The literature is abundant of different naming schemes
describing the same conceptualization, such as shape descriptors or geometric features but for the remaining of this document they will be referred to as point feature representations.

2.2描述子的定义
A feature descriptor is a representation of an image or an image patch that simplifies the image by extracting useful information and throwing away extraneous information.Typically, a feature descriptor converts an image of size width x height x 3 (channels ) to a feature vector / array of length n.
这里是二维图像中关于特征描述子的定义，推广到点云即可。
2.点云特征描述分类
2.1基于全局的特征描述
2.2基于局部的特征描述：基于点特征、基于直方图、基于变换
3.局部描述子（local descriptor=shape descriptors=geometric features=point feature representations）优劣的判定方法
如下：
理想情况下相同或相似的表面上的点的特征值非常相似，而不同表面的点的特征描述子差异明显。
（1）刚体变换（rigid transformations）即三维旋转和三维平移变化不会影响特征向量，即特征向量具有平移和旋转不变性。
（2）改变采样密度（varying sampling density）,原则上一个局部表面小块的采样密度不论大小，都应该具有相同的特征向量值，即特征向量具有抗密度干扰性。
（3）噪声（noise）,数据中有轻微噪声情况下，点特征表示在他的特征向量中必须保持相同或者相似的值，即特征向量对点云噪声具有稳健性。