c# - 一组边界框中的点分类

c# - 一组边界框中的点分类,第1张

我在3D空间中有一组边界框(矩形)。计算每个框的边界并将其存储在名为“RegionBounds”的字典中。此外,在名为“PointsToCategorize”的List中填充一组点。给定填充的List中的点(x,y,z)坐标和要检入的边界框,我可以检查该点是否在框内或不。问题是,这是一个很大的数据集。要检查的点数是1000,而边界框的数量是250-300。所以,如果我遍历每个给定点的每个边界框;所需的总时间为5-6分钟。有没有有效的方法可以更快地完成这个过程?如果可能的话,这样做的小代码会很棒

public struct iBounds  {

public double x1, x2;
public double y1, y2;
public double z1, z2;

}
public struct iPoint  {        

   public double x,y,z

}

Dictionary<String, iBounds> RegionBounds = new Dictionary<String, iBounds>();
List<iPoint> PointsToCategorize = new List<iPoint>();

int no_of_bounding_boxes = 300;
int no_of_points_to_categorize = 1000;

for (int i = 1; i <= no_of_bounding_boxes; i  )
{

  String boundingBoxName = "bound_"   i;
  iBounds boundingBox = new iBounds
    {

        x1 = Computed By Some Other method and Formulas,
        x2 = Computed By Some Other method and Formulas,
        y1 = Computed By Some Other method and Formulas,
        y2 = Computed By Some Other method and Formulas,
        z1 = Computed By Some Other method and Formulas,
        z2 = Computed By Some Other method and Formulas

    };

    RegionBounds.Add(boundingBoxName, boundingBox);
}



   ////////////Start of Output section /////////////////////////

 for(int i= 1; i < = PointsToCategorize.Count; i  ){

  foreach(var pair in RegionBounds)
   {
     String myboxNmame = pair.Key;
     iBounds myboxBounds = pair.Value;
      Console.WriteLine(PointInside(PointsToCategorize[i],myboxBounds).ToString());

  }
}

 ////////////// End of Output section //////////////////

private bool PointInside(iPoint mypoint, iBounds boxToBeCheckedIn)
{
    if (mypoint.x > boxToBeCheckedIn.x1) && (mypoint.x < boxToBeCheckedIn.x2){
        if (mypoint.y > boxToBeCheckedIn.y1) && (mypoint.y < boxToBeCheckedIn.y2){
            if (mypoint.z > boxToBeCheckedIn.z1) && (mypoint.z < boxToBeCheckedIn.z2){
                return true;
            }
        }
    }else{
        return false;
    }

}

最佳答案:

1 个答案:

答案 0 :(得分:0)

您可能希望使用OcTree或kD-tree数据结构,这比迭代所有框更有效。

另请参阅 2-D正交范围搜索部分中的this article,它具有非常好的可用技术和算法简历,可轻松扩展到3D

本文经用户投稿或网站收集转载,如有侵权请联系本站。

发表评论

0条回复