Dealing with grid like data structures is pretty common in collision detection tasks or eulerian/grid based solvers. This post is meant to provide helper functions such cases.

Compute the bounds of a grid given point data. In this case Thrust is used for its Transformed Reduce operation.

bin_edge = //specified value, represents the size of a grid cell

bbox res(pos[0], pos[0]);
bbox_transformation unary_op;
bbox_reduction binary_op;
res = thrust::transform_reduce(pos_marker.begin(), pos_marker.end(), unary_op, res, binary_op);

//Round to the nearest grid point, std::floor, and std::ceil can be used depending on the application

res.first.x = bin_edge * std::round(res.first.x / bin_edge);
res.first.y = bin_edge * std::round(res.first.y / bin_edge);
res.first.z = bin_edge * std::round(res.first.z / bin_edge);

res.second.x = bin_edge * std::round(res.second.x / bin_edge);
res.second.y = bin_edge * std::round(res.second.y / bin_edge);
res.second.z = bin_edge * std::round(res.second.z / bin_edge);


Grow the grid by a fixed amount if we need a buffer

real3 max_bounding_point = real3(res.second.x, res.second.y, res.second.z) + bin_edge * 4;
real3 min_bounding_point = real3(res.first.x, res.first.y, res.first.z) - bin_edge * 2;


Compute the properties of the grid

bin_edge = //specified value,
//The number of grid points along each axis
bins_per_axis = int3((max_bounding_point - min_bounding_point) / bin_edge);
//The number of total grid points
uint number_of_points = bins_per_axis.x * bins_per_axis.y * bins_per_axis.z;


Given a position, the length of the cell and the global minimum point, compute the grid node location along an axis. Call this function for each axis to get the 3d node location

inline int GridCoord(real x, real inv_bin_edge, real minimum) {
real l = x - minimum;
int c = std::round(l * inv_bin_edge);
return c;
}


Given the 3D index of a grid point determine the unique hash for that cell.

inline int GridHash(int x, int y, int z, const int3& bins_per_axis) {
return ((z * bins_per_axis.y) * bins_per_axis.x) + (y * bins_per_axis.x) + x;
}


Given the unique hash for a grid point determine the 3D index

static inline int3 GridDecode(int hash, const int3& bins_per_axis) {
int3 decoded_hash;
decoded_hash.x = hash % (bins_per_axis.x * bins_per_axis.y) % bins_per_axis.x;
decoded_hash.y = (hash % (bins_per_axis.x * bins_per_axis.y)) / bins_per_axis.x;
decoded_hash.z = hash / (bins_per_axis.x * bins_per_axis.y);
return decoded_hash;
}


Given the 3d Index of a grid point determine its actual location

static inline real3 NodeLocation(int i, int j, int k, real bin_edge, real3 min_bounding_point) { real3 node_location; node_location.x = i * bin_edge + min_bounding_point.x; node_location.y = j * bin_edge + min_bounding_point.y; node_location.z = k * bin_edge + min_bounding_point.z; return node_location; }

Putting it all together, looping over the neighboring nodes for a given location

    //Get the grid node associated with a specific point
const int cx = GridCoord(xi.x, inv_bin_edge, min_bounding_point.x);
const int cy = GridCoord(xi.y, inv_bin_edge, min_bounding_point.y);
const int cz = GridCoord(xi.z, inv_bin_edge, min_bounding_point.z);
//Loop over the 1-ring neighbors
for (int i = cx - 1; i <= cx + 1; ++i) {
for (int j = cy - 1; j <= cy + 1; ++j) {
for (int k = cz - 1; k <= cz + 1; ++k) {
const int current_node = GridHash(i, j, k, bins_per_axis);
real3 current_node_location = NodeLocation(i, j, k, bin_edge, min_bounding_point);
// Do stuff here
}
}
}