点云生成 [英] Point Cloud Generation

问题描述

• 我具有3D几何形状，必须将其转换为点云.
• 可以将所得的点云视为等效于从对象的激光扫描输出的点云.
• 不需生成网格物体
• 生成的点可以均匀分布，也可以随机分布-没关系
• 可以以3-D数学公式的形式提供3-D形状
• 必须使用MATLAB来完成

推荐答案

没有示例就很难回答，但听起来您只想执行

It's difficult to answer without an example but it sounds like you just want to perform a montecarlo simulation?

假设您的形状由函数f定义，并且您将X，Y限制存储在两个元素向量中，例如xlim = [-10 10]，即此形状的所有可能x值都在x = -10和x = 10之间，那么我建议您使f返回某种错误代码(如果没有特定xy的值)一对.我将假定为NaN.因此，f(x,y)是您正在编写的函数，如果可以，则返回z，如果不能，则返回NaN

Lets say your shape is defined by the function f and that you have X, Y limits stored in two element vector e.g. xlim = [-10 10] i.e. all possible x values of this shape lie between x = -10 and x = 10 then I would suggest that you make f return some sort of error code if there is no value for a specific x-y pair. I'm going to assume that will be NaN. So f(x,y) is a function you are writing that either returns a z if it can or NaN if it can't

n= 10000;
counter  = 1;
shape = nan(n, 3)
while counter < n
   x = rand*diff(xlim) + mean(xlmin);
   y = rand*diff(ylim) + mean(ylim);
   z = f(x,y)
   if ~isnan(z)
      shape(counter, :) = [x, y, z];
      counter = counter + 1    
   end
end

因此，上面的代码将在整个形状中随机生成10000个(非唯一的，但很容易适应)点.

So the above code will generate 10000 (non unique, but that's easily adapted for) points randomly sample across your shape.

现在，键入此代码后，我意识到也许您的形状实际上并不那么大，也许您可​​以对其进行均匀采样而不是随机采样:

Now after typing this I realise that perhaps your shape is actually not all that big and maybe you can uniformly sample it rather than randomly:

for x = xlim(1):xstep:xlim(2)
   for y = ylim(1):ystep:ylim(2)
       shape(counter, :) = [x, y, f(x,y)];
   end
end 

或者如果您编写f进行矢量化(首选)

or if you write f to be vectorized (preferable)

shape = [(xlim(1):xstep:xlim(2))', (ylim(1):ystep:ylim(2))', f(xlim(1):xstep:xlim(2), ylim(1):ystep:ylim(2));

然后任意选择

shape(isnan(shape(:, 3), :) = []; %remove the points that fell outside the shape

这篇关于点云生成的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！