Very fast 3D distance check?

Question

Is there a way to do a quick and dirty 3D distance check where the results are rough, but it is very very fast? I need to do depth sorting. I use STL sort like this

Answer 1

What I usually do is first filter by Manhattan distance

float CBox::Within3DManhattanDistance( Vec3 c1, Vec3 c2, float distance )
{
    float dx = abs(c2.x - c1.x);
    float dy = abs(c2.y - c1.y);
    float dz = abs(c2.z - c1.z);

    if (dx > distance) return 0; // too far in x direction
    if (dy > distance) return 0; // too far in y direction
    if (dz > distance) return 0; // too far in z direction

    return 1; // we're within the cube
}

Actually you can optimize this further if you know more about your environment. For example, in an environment where there is a ground like a flight simulator or a first person shooter game, the horizontal axis is very much larger than the vertical axis. In such an environment, if two objects are far apart they are very likely separated more by the x and y axis rather than the z axis (in a first person shooter most objects share the same z axis). So if you first compare x and y you can return early from the function and avoid doing extra calculations:

float CBox::Within3DManhattanDistance( Vec3 c1, Vec3 c2, float distance )
{
    float dx = abs(c2.x - c1.x);
    if (dx > distance) return 0; // too far in x direction

    float dy = abs(c2.y - c1.y);
    if (dy > distance) return 0; // too far in y direction

    // since x and y distance are likely to be larger than
    // z distance most of the time we don't need to execute
    // the code below:

    float dz = abs(c2.z - c1.z);
    if (dz > distance) return 0; // too far in z direction

    return 1; // we're within the cube
}

Sorry, I didn't realize the function is used for sorting. You can still use Manhattan distance to get a very rough first sort:

float CBox::ManhattanDistance( Vec3 c1, Vec3 c2 )
{
    float dx = abs(c2.x - c1.x);
    float dy = abs(c2.y - c1.y);
    float dz = abs(c2.z - c1.z);

    return dx+dy+dz;
}

After the rough first sort you can then take the topmost results, say the top 10 closest players, and re-sort using proper distance calculations.

Answer 2

If this is simply a value for sorting, then you can swap the sqrt() for a abs(). If you need to compare distances against set values, get the square of that value.

E.g. instead of checking sqrt(...) against a, you can compare abs(...) against a*a.

Answer 3

You may want to consider caching the distance between the player and the object as you calculate it, and then use that in your sortfunc. This would depend upon how many times your sort function looks at each object, so you might have to profile to be sure.

What I'm getting at is that your sort function might do something like this:

compare(a,b);
compare(a,c);
compare(a,d);

and you would calculate the distance between the player and 'a' every time.

As others have mentioned, you can leave out the sqrt in this case.

Answer 4

If you worry about performance, you should also take care of the way you send your arguments:

float Get3dDistance( Vec3 c1, Vec3 c2 );

implies two copies of Vec3 structure. Use references instead:

float Get3dDistance( Vec3 const & c1, Vec3 const & c2 );

Answer 5

Depending slightly on the number of points that you are being used to compare with, what is below is pretty much guaranteed to be the get the list of points in approximate order assuming all points change at all iteration.

1) Rewrite the array into a single list of Manhattan distances with out[ i ] = abs( posn[ i ].x - player.x ) + abs( posn[ i ].y - player.y ) + abs( posn[ i ].z - player.z );

2) Now you can use radix sort on floating point numbers to order them.

Note that in practice this is going to be a lot faster than sorting the list of 3d positions because it significantly reduces the memory bandwidth requirements in the sort operation which all of the time is going to be spend and in which unpredictable accesses and writes are going to occur. This will run on O(N) time.

If many of the points are stationary at each direction there are far faster algorithms like using KD-Trees, although implementation is quite a bit more complex and it is much harder to get good memory access patterns.

Answer 6

You can leave out the square root because for all positive (or really, non-negative) numbers x and y, if sqrt(x) < sqrt(y) then x < y. Since you're summing squares of real numbers, the square of every real number is non-negative, and the sum of any positive numbers is positive, the square root condition holds.

You cannot eliminate the multiplication, however, without changing the algorithm. Here's a counterexample: if x is (3, 1, 1) and y is (4, 0, 0), |x| < |y| because sqrt(1*1+1*1+3*3) < sqrt(4*4+0*0+0*0) and 1*1+1*1+3*3 < 4*4+0*0+0*0, but 1+1+3 > 4+0+0.

Since modern CPUs can compute a dot product faster than they can actually load the operands from memory, it's unlikely that you would have anything to gain by eliminating the multiply anyway (I think the newest CPUs have a special instruction that can compute a dot product every 3 cycles!).

I would not consider changing the algorithm without doing some profiling first. Your choice of algorithm will heavily depend on the size of your dataset (does it fit in cache?), how often you have to run it, and what you do with the results (collision detection? proximity? occlusion?).