Related
Edit: So I found a page related to rasterizing a trapezoid https://cse.taylor.edu/~btoll/s99/424/res/ucdavis/GraphicsNotes/Rasterizing-Polygons/Rasterizing-Polygons.html but am still trying to figure out if I can just do the edges
I am trying to generate points for the corners of an arbitrary N-gon. Where N is a non-zero positive integer. But I am trying to do so efficiently without the need of division and trig. I am thinking that there is probably some of sort of Bresenham's type algorithm for this but I cannot seem to find anything.
The only thing I can find on stackoverflow is this but the internal angle increments are found by using 2*π/N:
How to draw a n sided regular polygon in cartesian coordinates?
Here was the algorithm from that page in C
float angle_increment = 2*PI / n_sides;
for(int i = 0; i < n_sides; ++i)
{
float x = x_centre + radius * cos(i*angle_increment +start_angle);
float y = y_centre + radius * sin(i*angle_increment +start_angle);
}
So is it possible to do so without any division?
There's no solution which avoids calling sin and cos, aside from precomputing them for all useful values of N. However, you only need to do the computation once, regardless of N. (Provided you know where the centre of the polygon is. You might need a second computation to figure out the coordinates of the centre.) Every vertex can be computed from the previous vertex using the same rotation matrix.
The lookup table of precomputed values is not an unreasonable solution, since at some sufficiently large value of N the polygon becomes indistinguishable from a circle. So you probably only need a lookup table of a few hundred values.
I am trying to generate uniform random points on the surface of a unit sphere for a Monte Carlo ray tracing program. When I say uniform I mean the points are uniformly distributed with respect to surface area. My current methodology is to calculate uniform random points on a hemisphere pointing in the positive z axis and base in the x-y plane.
The random point on the hemisphere represents the direction of emission of thermal radiation for a diffuse grey emitter.
I achieve the correct result when I use the following calculation :
Note : dsfmt* is will return a random number between 0 and 1.
azimuthal = 2*PI*dsfmt_genrand_close_open(&dsfmtt);
zenith = asin(sqrt(dsfmt_genrand_close_open(&dsfmtt)));
// Calculate the cartesian point
osRay.c._x = sin(zenith)*cos(azimuthal);
osRay.c._y = sin(zenith)*sin(azimuthal);
osRay.c._z = cos(zenith);
However this is quite slow and profiling suggests that it takes up a large proportion of run time. Therefore I sought out some alternative methods:
The Marsaglia 1972 rejection method
do {
x1 = 2.0*dsfmt_genrand_open_open(&dsfmtt)-1.0;
x2 = 2.0*dsfmt_genrand_open_open(&dsfmtt)-1.0;
S = x1*x1 + x2*x2;
} while(S > 1.0f);
osRay.c._x = 2.0*x1*sqrt(1.0-S);
osRay.c._y = 2.0*x2*sqrt(1.0-S);
osRay.c._z = abs(1.0-2.0*S);
Analytical cartesian coordinate calculation
azimuthal = 2*PI*dsfmt_genrand_close_open(&dsfmtt);
u = 2*dsfmt_genrand_close_open(&dsfmtt) -1;
w = sqrt(1-u*u);
osRay.c._x = w*cos(azimuthal);
osRay.c._y = w*sin(azimuthal);
osRay.c._z = abs(u);
While these last two methods run serval times faster than the first, when I use them I get results which indicate that they are not generating uniform random points on the surface of a sphere but rather are giving a distribution which favours the equator.
Additionally the last two methods give identical final results however I am certain that they are incorrect as I am comparing against an analytical solution.
Every reference I have found indicates that these methods do produce uniform distributions however I do not achieve the correct result.
Is there an error in my implementation or have I missed a fundamental idea in the second and third methods?
The simplest way to generate a uniform distribution on the unit sphere (whatever its dimension is) is to draw independent normal distributions and normalize the resulting vector.
Indeed, for example in dimension 3, e^(-x^2/2) e^(-y^2/2) e^(-z^2/2) = e^(-(x^2 + y^2 + z^2)/2) so the joint distribution is invariant by rotations.
This is fast if you use a fast normal distribution generator (either Ziggurat or Ratio-Of-Uniforms) and a fast normalization routine (google for "fast inverse square root). No transcendental function call is required.
Also, the Marsaglia is not uniform on the half sphere. You'll have more points near the equator since the correspondence point on the 2D disc <-> point on the half sphere is not isometric. The last one seems correct though (however I didn't make the calculation to ensure this).
If you take a horizontal slice of the unit sphere, of height h, its surface area is just 2 pi h. (This is how Archimedes calculated the surface area of a sphere.) So the z-coordinate is uniformly distributed in [0,1]:
azimuthal = 2*PI*dsfmt_genrand_close_open(&dsfmtt);
osRay.c._z = dsfmt_genrand_close_open(&dsfmtt);
xyproj = sqrt(1 - osRay.c._z*osRay.c._z);
osRay.c._x = xyproj*cos(azimuthal);
osRay.c._y = xyproj*sin(azimuthal);
Also you might be able to save some time by calculating cos(azimuthal) and sin(azimuthal) together -- see this stackoverflow question for discussion.
Edited to add: OK, I see now that this is just a slight tweak of your third method. But it cuts out a step.
This should be quick if you have a fast RNG:
// RNG::draw() returns a uniformly distributed number between -1 and 1.
void drawSphereSurface(RNG& rng, double& x1, double& x2, double& x3)
{
while (true) {
x1 = rng.draw();
x2 = rng.draw();
x3 = rng.draw();
const double radius = sqrt(x1*x1 + x2*x2 + x3*x3);
if (radius > 0 && radius < 1) {
x1 /= radius;
x2 /= radius;
x3 /= radius;
return;
}
}
}
To speed it up, you can move the sqrt call inside the if block.
Have you tried getting rid of asin?
azimuthal = 2*PI*dsfmt_genrand_close_open(&dsfmtt);
sin2_zenith = dsfmt_genrand_close_open(&dsfmtt);
sin_zenith = sqrt(sin2_zenith);
// Calculate the cartesian point
osRay.c._x = sin_zenith*cos(azimuthal);
osRay.c._y = sin_zenith*sin(azimuthal);
osRay.c._z = sqrt(1 - sin2_zenith);
I think the problem you are having with non-uniform results is because in polar coordinates, a random point on the circle is not uniformly distributed on the radial axis. If you look at the area on [theta, theta+dtheta]x[r,r+dr], for fixed theta and dtheta, the area will be different of different values of r. Intuitivly, there is "more area" further out from the center. Thus, you need to scale your random radius to account for this. I haven't got the proof lying around, but the scaling is r=R*sqrt(rand), with R being the radius of the circle and rand begin the random number.
The second and third methods do in fact produce uniformly distributed random points on the surface of a sphere with the second method (Marsaglia 1972) producing the fastest run times at around twice the speed on an Intel Xeon 2.8 GHz Quad-Core.
As noted by Alexandre C there is an additional method using the normal distribution which expands to n-spheres better than the methods I have presented.
This link will give you further information on selecting uniformly distributed random points on the surface of a sphere.
My initial method as pointed out by TonyK does not produce uniformly distributed points and rather bias's the poles when generating the random points. This is required by the problem I am trying to solve however I simply assumed it would generate uniformly random points. As suggested by Pablo this method can be optimised by removing the asin() call to reduce run time by around 20%.
1st try (wrong)
point=[rand(-1,1),rand(-1,1),rand(-1,1)];
len = length_of_vector(point);
EDITED:
What about?
while(1)
point=[rand(-1,1),rand(-1,1),rand(-1,1)];
len = length_of_vector(point);
if( len > 1 )
continue;
point = point / len
break
Acception is here approx 0.4. Than mean that you will reject 60% of solutions.
I am implementing a marching cubes algorithm generally based on the implementation of Paul Bourke with some major adjustments:
precalculation of the scalarfield (floating point values)
avoiding duplicated vertices in the final list using a std::map
vertex storing to visualize the final mesh in Ogre3D
Basically I changed nearly 80% of his code. My resulting mesh has some ugly terrasses and I am not sure how to avoid them. I assumed that using floating points for the scalar field would do the job. Is this a common effect? How can you avoid it?
calculating the vertex positions on the edges. (cell.val[p1] contains the scalar value for the given vertex):
//if there is an intersection on this edge
if (cell.iEdgeFlags & (1 << iEdge))
{
const int* edge = a2iEdgeConnection[iEdge];
int p1 = edge[0];
int p2 = edge[1];
//find the approx intersection point by linear interpolation between the two edges and the density value
float length = cell.val[p1] / (cell.val[p2] + cell.val[p1]);
asEdgeVertex[iEdge] = cell.p[p1] + length * (cell.p[p2] - cell.p[p1]);
}
You can find the complete sourcecode here: https://github.com/DieOptimistin/MarchingCubes
I use Ogre3D as library for this example.
As Andy Newmann said, the devil was in the linear interpolation. Correct is:
float offset;
float delta = cell.val[p2] - cell.val[p1];
if (delta == 0) offset = 0.5;
else offset = (mTargetValue - cell.val[p1]) / delta;
asEdgeVertex[iEdge] = cell.p[p1] + offset* (cell.p[p2] - cell.p[p1]);
I'm working on procedurally generating patches of dirt using randomized fractals for a video game. I've already generated a height map using the midpoint displacement algorithm and saved it to a texture. I have some ideas for how to turn that into a texture of normals, but some feedback would be much appreciated.
My height texture is currently a 257 x 257 gray-scale image (height values are scaled for visibility purposes):
My thinking is that each pixel of the image represents a lattice coordinate in a 256 x 256 grid (hence, why there are 257 x 257 heights). That would mean that the normal at coordinate (i, j) is determined by the heights at (i, j), (i, j + 1), (i + 1, j), and (i + 1, j + 1) (call those A, B, C, and D, respectively).
So given the 3D coordinates of A, B, C, and D, would it make sense to:
split the four into two triangles: ABC and BCD
calculate the normals of those two faces via cross product
split into two triangles: ACD and ABD
calculate the normals of those two faces
average the four normals
...or is there a much easier method that I'm missing?
Example GLSL code from my water surface rendering shader:
#version 130
uniform sampler2D unit_wave
noperspective in vec2 tex_coord;
const vec2 size = vec2(2.0,0.0);
const ivec3 off = ivec3(-1,0,1);
vec4 wave = texture(unit_wave, tex_coord);
float s11 = wave.x;
float s01 = textureOffset(unit_wave, tex_coord, off.xy).x;
float s21 = textureOffset(unit_wave, tex_coord, off.zy).x;
float s10 = textureOffset(unit_wave, tex_coord, off.yx).x;
float s12 = textureOffset(unit_wave, tex_coord, off.yz).x;
vec3 va = normalize(vec3(size.xy,s21-s01));
vec3 vb = normalize(vec3(size.yx,s12-s10));
vec4 bump = vec4( cross(va,vb), s11 );
The result is a bump vector: xyz=normal, a=height
My thinking is that each pixel of the image represents a lattice coordinate in a 256 x 256 grid (hence, why there are 257 x 257 heights). That would mean that the normal at coordinate (i, j) is determined by the heights at (i, j), (i, j + 1), (i + 1, j), and (i + 1, j + 1) (call those A, B, C, and D, respectively).
No. Each pixel of the image represents a vertex of the grid, so intuitively, from symmetry, its normal is determined by heights of neighboring pixels (i-1,j), (i+1,j), (i,j-1), (i,j+1).
Given a function f : ℝ2 → ℝ that describes a surface in ℝ3, a unit normal at (x,y) is given by
v = (−∂f/∂x, −∂f/∂y, 1) and n = v/|v|.
It can be proven that the best approximation to ∂f/∂x by two samples is archived by:
∂f/∂x(x,y) = (f(x+ε,y) − f(x−ε,y))/(2ε)
To get a better approximation you need to use at least four points, thus adding a third point (i.e. (x,y)) doesn't improve the result.
Your hightmap is a sampling of some function f on a regular grid. Taking ε=1 you get:
2v = (f(x−1,y) − f(x+1,y), f(x,y−1) − f(x,y+1), 2)
Putting it into code would look like:
// sample the height map:
float fx0 = f(x-1,y), fx1 = f(x+1,y);
float fy0 = f(x,y-1), fy1 = f(x,y+1);
// the spacing of the grid in same units as the height map
float eps = ... ;
// plug into the formulae above:
vec3 n = normalize(vec3((fx0 - fx1)/(2*eps), (fy0 - fy1)/(2*eps), 1));
A common method is using a Sobel filter for a weighted/smooth derivative in each direction.
Start by sampling a 3x3 area of heights around each texel (here, [4] is the pixel we want the normal for).
[6][7][8]
[3][4][5]
[0][1][2]
Then,
//float s[9] contains above samples
vec3 n;
n.x = scale * -(s[2]-s[0]+2*(s[5]-s[3])+s[8]-s[6]);
n.y = scale * -(s[6]-s[0]+2*(s[7]-s[1])+s[8]-s[2]);
n.z = 1.0;
n = normalize(n);
Where scale can be adjusted to match the heightmap real world depth relative to its size.
If you think of each pixel as a vertex rather than a face, you can generate a simple triangular mesh.
+--+--+
|\ |\ |
| \| \|
+--+--+
|\ |\ |
| \| \|
+--+--+
Each vertex has an x and y coordinate corresponding to the x and y of the pixel in the map. The z coordinate is based on the value in the map at that location. Triangles can be generated explicitly or implicitly by their position in the grid.
What you need is the normal at each vertex.
A vertex normal can be computed by taking an area-weighted average of the surface normals for each of the triangles that meet at that point.
If you have a triangle with vertices v0, v1, v2, then you can use a vector cross product (of two vectors that lie on two of the sides of the triangle) to compute a vector in the direction of the normal and scaled proportionally to the area of the triangle.
Vector3 contribution = Cross(v1 - v0, v2 - v1);
Each of your vertices that aren't on the edge will be shared by six triangles. You can loop through those triangles, summing up the contributions, and then normalize the vector sum.
Note: You have to compute the cross products in a consistent way to make sure the normals are all pointing in the same direction. Always pick two sides in the same order (clockwise or counterclockwise). If you mix some of them up, those contributions will be pointing in the opposite direction.
For vertices on the edge, you end up with a shorter loop and a lot of special cases. It's probably easier to create a border around your grid of fake vertices and then compute the normals for the interior ones and discard the fake borders.
for each interior vertex V {
Vector3 sum(0.0, 0.0, 0.0);
for each of the six triangles T that share V {
const Vector3 side1 = T.v1 - T.v0;
const Vector3 side2 = T.v2 - T.v1;
const Vector3 contribution = Cross(side1, side2);
sum += contribution;
}
sum.Normalize();
V.normal = sum;
}
If you need the normal at a particular point on a triangle (other than one of the vertices), you can interpolate by weighing the normals of the three vertices by the barycentric coordinates of your point. This is how graphics rasterizers treat the normal for shading. It allows a triangle mesh to appear like smooth, curved surface rather than a bunch of adjacent flat triangles.
Tip: For your first test, use a perfectly flat grid and make sure all of the computed normals are pointing straight up.
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:
bool sortfunc(CBox* a, CBox* b)
{
return a->Get3dDistance(Player.center,a->center) <
b->Get3dDistance(Player.center,b->center);
}
float CBox::Get3dDistance( Vec3 c1, Vec3 c2 )
{
//(Dx*Dx+Dy*Dy+Dz*Dz)^.5
float dx = c2.x - c1.x;
float dy = c2.y - c1.y;
float dz = c2.z - c1.z;
return sqrt((float)(dx * dx + dy * dy + dz * dz));
}
Is there possibly a way to do it without a square root or possibly without multiplication?
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?).
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.
Here's an equation that might help you get rid of both sqrt and multiply:
max(|dx|, |dy|, |dz|) <= distance(dx,dy,dz) <= |dx| + |dy| + |dz|
This gets you a range estimate for the distance which pins it down to within a factor of 3 (the upper and lower bounds can differ by at most 3x). You can then sort on, say, the lower number. You then need to process the array until you reach an object which is 3x farther away than the first obscuring object. You are then guaranteed to not find any object that is closer later in the array.
By the way, sorting is overkill here. A more efficient way would be to make a series of buckets with different distance estimates, say [1-3], [3-9], [9-27], .... Then put each element in a bucket. Process the buckets from smallest to largest until you reach an obscuring object. Process 1 additional bucket just to be sure.
By the way, floating point multiply is pretty fast nowadays. I'm not sure you gain much by converting it to absolute value.
I'm disappointed that the great old mathematical tricks seem to be getting lost. Here is the answer you're asking for. Source is Paul Hsieh's excellent web site: http://www.azillionmonkeys.com/qed/sqroot.html . Note that you don't care about distance; you will do fine for your sort with square of distance, which will be much faster.
In 2D, we can get a crude approximation of the distance metric without a square root with the formula:
distanceapprox (x, y) =
which will deviate from the true answer by at most about 8%. A similar derivation for 3 dimensions leads to:
distanceapprox (x, y, z) =
with a maximum error of about 16%.
However, something that should be pointed out, is that often the distance is only required for comparison purposes. For example, in the classical mandelbrot set (z←z2+c) calculation, the magnitude of a complex number is typically compared to a boundary radius length of 2. In these cases, one can simply drop the square root, by essentially squaring both sides of the comparison (since distances are always non-negative). That is to say:
√(Δx2+Δy2) < d is equivalent to Δx2+Δy2 < d2, if d ≥ 0
I should also mention that Chapter 13.2 of Richard G. Lyons's "Understanding Digital Signal Processing" has an incredible collection of 2D distance algorithms (a.k.a complex number magnitude approximations). As one example:
Max = x > y ? x : y;
Min = x < y ? x : y;
if ( Min < 0.04142135Max )
|V| = 0.99 * Max + 0.197 * Min;
else
|V| = 0.84 * Max + 0.561 * Min;
which has a maximum error of 1.0% from the actual distance. The penalty of course is that you're doing a couple branches; but even the "most accepted" answer to this question has at least three branches in it.
If you're serious about doing a super fast distance estimate to a specific precision, you could do so by writing your own simplified fsqrt() estimate using the same basic method as the compiler vendors do, but at a lower precision, by doing a fixed number of iterations. For example, you can eliminate the special case handling for extremely small or large numbers, and/or also reduce the number of Newton-Rapheson iterations. This was the key strategy underlying the so-called "Quake 3" fast inverse square root implementation -- it's the classic Newton algorithm with exactly one iteration.
Do not assume that your fsqrt() implementation is slow without benchmarking it and/or reading the sources. Most modern fsqrt() library implementations are branchless and really damned fast. Here for example is an old IBM floating point fsqrt implementation. Premature optimization is, and always will be, the root of all evil.
Note that for 2 (non-negative) distances A and B, if sqrt(A) < sqrt(B), then A < B. Create a specialized version of Get3DDistance() (GetSqrOf3DDistance()) that does not call sqrt() that would be used only for the sortfunc().
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 );
You could compare squares of distances instead of the actual distances, since d2 = (x1-x2)2 + (y1-y2)2+ (z1-z2)2. It doesn't get rid of the multiplication, but it does eliminate the square root operation.
How often are the input vectors updated and how often are they sorted? Depending on your design, it might be quite efficient to extend the "Vec3" class with a pre-calculated distance and sort on that instead. Especially relevant if your implementation allows you to use vectorized operations.
Other than that, see the flipcode.com article on approximating distance functions for a discussion on yet another approach.
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.
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.
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.
If you could center your coordinates around the player, use spherical coordinates? Then you could sort by the radius.
That's a big if, though.
If your operation happens a lot, it might be worth to put it into some 3D data structure. You probably need the distance sorting to decide which object is visible, or some similar task. In order of complexity you can use:
Uniform (cubic) subdivision
Divide the used space into cells, and assign the objects to the cells. Fast access to element, neighbours are trivial, but empty cells take up a lot of space.
Quadtree
Given a threshold, divide used space recursively into four quads until less then threshold number of object is inside. Logarithmic access element if objects don't stack upon each other, neighbours are not hard to find, space efficient solution.
Octree
Same as Quadtree, but divides into 8, optimal even if objects are above each other.
Kd tree
Given some heuristic cost function, and a threshold, split space into two halves with a plane where the cost function is minimal. (Eg.: same amount of objects at each side.) Repeat recursively until threshold reached. Always logarithmic, neighbours are harder to get, space efficient (and works in all dimensions).
Using any of the above data structures, you can start from a position, and go from neighbour to neighbour to list the objects in increasing distance. You can stop at desired cut distance. You can also skip cells that cannot be seen from the camera.
For the distance check, you can do one of the above mentioned routines, but ultimately they wont scale well with increasing number of objects. These can be used to display data that takes hundreds of gigabytes of hard disc space.