从3个视图中创建3D对象 [英] Create 3D Object out of 3 views

问题描述

我想从3个视图中计算一个3D对象。原理如下图所示。

每个视图都存储在具有二进制值的2维矩阵表示对象。 3D对象应该以二进制值存储在3维矩阵中（True：该像素代表对象质量，False：该像素是白色空间）。
如何通过简单的矩阵运算来实现这一点？

>可以例如看起来像 [[0,0,0,0]，[0,1,1,0]，[0,1,1,0]，[0,0,0， 0]] 。

解决方案

c 然后：

  result = a [None，：，：]& b [:,无，：]& c [：：：，None] 
  

绕轴旋转以适应输入

a ， b 和 c 被假定为以下形式：

  np.array（[[0,0,0,0] ，[0,1,1,0]，[0,1,1,0]，[0,0,0,0]]，dtype = np.bool）
  

I'd like to calculate an 3D object out of the 3 views. The principle is shown in following figure.

Each view is stored in a 2 dimensional matrix with binary values representing the object. The 3D object should be stored in a 3 dimensional matrix also with binary values (True: this pixel is representing object mass, False: this pixel is white space). How can I realize this with simply numpy matrix operations?

The three views a,b and c can for example look like [[0,0,0,0],[0,1,1,0],[0,1,1,0],[0,0,0,0]].

解决方案

If your views are a, b, c then:

result = a[None, :, :] & b[:, None, :] & c[:, :, None]

Shuffle round the axes to suit the input

a, b and c are assumed to be of the form:

np.array([[0,0,0,0],[0,1,1,0],[0,1,1,0],[0,0,0,0]], dtype=np.bool)

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