Java 生成不重复的“Vector3”

Question

I am trying to generate a list of a hundred thousand random points in 3d space within a 3d boundary without having any of the points occupy the same position. I'm literally trying to create a non-repeating Vector3 generator. Is there any efficient way of doing this? Also, it is okay if these points are not evenly distributed, it is actually preferable if they are someone clustered here there, just as long as they do not occupy the same position.

To clarify I am not trying to generate 300,000 unique points. But instead 100,000 3d points. So a vector values of (0, 0, 0) and (0, 0, 1) is acceptable. But (4, 4, 4) and (4, 4, 4) is unacceptable.

public class Vector3
{
    public float x;
    public float y;
    public float z;

    Vector3(float x, float y, float z)
    {
        this.x = x;
        this.y = y;
        this.z = z;
    }

    public static ArrayList<Vector3> generateVector3s()
    {
        ArrayList<Vector3> tempVector3List = new ArrayList<>();
        
        for (int i = 0; i < 100000; i++)
        {   
            tempVector3List.add(new Vector3(RANDOMVALUE, RANDOMVALUE, RANDOMVALUE));
        }
        
        return tempVector3List ;
    }
}

Answer 1

First, generation of Random Numbers in Java:

Geeks for Geeks on Random Numbers in Java

What I might do is generate a value then use the triplet as a key for a hashset. If it exists, the value is already there, and toss it and try again. This will ensure uniqueness and should be relatively efficient.

If you want your datapoints spread equally about your problem space, you might need a fancier algorithm.

Answer 2

It sounds like you are looking for a 3D version of Poisson disc sampling, which guarantees points will not cluster in the same areas.

The algorithm is fairly complicated so I won't describe it here fully, but the basic idea is that you choose a seed point and grow outwards by always adding points that are at least a given distance away from the existing points.

You can find detailed descriptions and implementations here:

• https://bost.ocks.org/mike/algorithms/
• http://gregschlom.com/devlog/2014/06/29/Poisson-disc-sampling-Unity.html
• https://github.com/kchapelier/poisson-disk-sampling
• https://youtu.be/7WcmyxyFO7o
• https://youtu.be/flQgnCUxHlw