```lua
-- Dijkstra's Algorithm in 3D implemented in LuaJIT for MoonLoader SA-MP
-- This implementation uses the LuaJIT FFI library for performance optimization

local ffi = require("ffi")

-- Define a simple priority queue using a binary heap
local PriorityQueue = {}
PriorityQueue.__index = PriorityQueue

function PriorityQueue.new()
    return setmetatable({ heap = {} }, PriorityQueue)
end

function PriorityQueue:push(priority, item)
    local heap = self.heap
    local node = { priority = priority, item = item }
    table.insert(heap, node)
    self:_siftUp(#heap)
end

function PriorityQueue:pop()
    local heap = self.heap
    if #heap == 0 then return nil end
    local root = heap[1]
    local last = table.remove(heap)
    if #heap > 0 then
        heap[1] = last
        self:_siftDown(1)
    end
    return root.priority, root.item
end

function PriorityQueue:_siftUp(idx)
    local heap = self.heap
    while idx > 1 do
        local parent = math.floor(idx / 2)
        if heap[idx].priority >= heap[parent].priority then break end
        heap[idx], heap[parent] = heap[parent], heap[idx]
        idx = parent
    end
end

function PriorityQueue:_siftDown(idx)
    local heap = self.heap
    local size = #heap
    while true do
        local left = idx * 2
        local right = left + 1
        local smallest = idx

        if left <= size and heap[left].priority < heap[smallest].priority then
            smallest = left
        end
        if right <= size and heap[right].priority < heap[smallest].priority then
            smallest = right
        end
        if smallest == idx then break end
        heap[idx], heap[smallest] = heap[smallest], heap[idx]
        idx = smallest
    end
end

function PriorityQueue:isEmpty()
    return #self.heap == 0
end

-- Implement Dijkstra's algorithm in 3D
function dijkstra3D(grid, gridSize, start, goal)
    --[[
    grid: a 3D array of traversal costs/accessibility
          grid[z][y][x] gives the cost to move to cell (x, y, z)
          Cells with cost == math.huge are considered impassable
    gridSize: a table { Nx, Ny, Nz }, grid dimensions
    start: { x, y, z } starting coordinates
    goal: { x, y, z } ending coordinates
    Returns:
        distances: a 1D FFI array of minimum distances from start
        path: table of coordinates representing the shortest path
    ]]

    -- Initialize
    local Nx, Ny, Nz = gridSize[1], gridSize[2], gridSize[3]
    local totalSize = Nx * Ny * Nz
    local distances = ffi.new("double[?]", totalSize)
    local visited = ffi.new("bool[?]", totalSize)
    local cameFrom = {}

    for i = 0, totalSize - 1 do
        distances[i] = math.huge
        visited[i] = false
    end

    -- Function to map 3D coordinates to 1D index
    local function index(x, y, z)
        return x + y * Nx + z * Nx * Ny
    end

    -- Define possible movements (6-directional)
    local directions = {
        {1, 0, 0},
        {-1, 0, 0},
        {0, 1, 0},
        {0, -1, 0},
        {0, 0, 1},
        {0, 0, -1}
    }

    -- Start position
    local sx, sy, sz = start[1], start[2], start[3]
    local ex, ey, ez = goal[1], goal[2], goal[3]

    -- Priority queue: (distance, {x, y, z})
    local frontier = PriorityQueue.new()
    frontier:push(0, { sx, sy, sz })

    local startIdx = index(sx, sy, sz)
    distances[startIdx] = 0
    cameFrom[startIdx] = nil

    -- Main loop
    while not frontier:isEmpty() do
        local currentDist, current = frontier:pop()
        local x, y, z = current[1], current[2], current[3]
        local idx = index(x, y, z)

        if not visited[idx] then
            visited[idx] = true

            -- Goal check
            if x == ex and y == ey and z == ez then
                break
            end

            -- Explore neighbors
            for _, dir in ipairs(directions) do
                local nx, ny, nz = x + dir[1], y + dir[2], z + dir[3]
                if nx >= 0 and nx < Nx and ny >= 0 and ny < Ny and nz >= 0 and nz < Nz then
                    local nIdx = index(nx, ny, nz)
                    local cost = grid[nz][ny][nx]
                    if not visited[nIdx] and cost ~= math.huge then
                        local newDist = currentDist + cost
                        if newDist < distances[nIdx] then
                            distances[nIdx] = newDist
                            cameFrom[nIdx] = { x, y, z }
                            frontier:push(newDist, { nx, ny, nz })
                        end
                    end
                end
            end
        end
    end

    -- Reconstruct path
    local path = {}
    local goalIdx = index(ex, ey, ez)
    if distances[goalIdx] ~= math.huge then
        local x, y, z = ex, ey, ez
        while not (x == sx and y == sy and z == sz) do
            table.insert(path, 1, { x, y, z }) -- Insert at the beginning
            local prev = cameFrom[index(x, y, z)]
            if not prev then break end
            x, y, z = prev[1], prev[2], prev[3]
        end
        table.insert(path, 1, { sx, sy, sz })
    else
        path = nil -- No path found
    end

    return distances, path
end

-- Example usage
function main()
    while not isSampAvailable() do
        wait(0)
    end

    local Nx, Ny, Nz = 10, 10, 5 -- Define grid size
    local grid = {}
    for z = 0, Nz - 1 do
        grid[z] = {}
        for y = 0, Ny - 1 do
            grid[z][y] = {}
            for x = 0, Nx - 1 do
                grid[z][y][x] = 1 -- Default cost
            end
        end
    end

    grid[2][5][5] = math.huge -- Create obstacles by setting cost to math.huge -- Impassable cell at (5, 5, 2)
    local start = {0, 0, 0}
    local goal = {9, 9, 4}
    local _, path = dijkstra3D(grid, {Nx, Ny, Nz}, start, goal)
    if path then
        print("Path found:")
        for i, pos in ipairs(path) do
            print(string.format("Step %d: (%d, %d, %d)", i, pos[1], pos[2], pos[3]))
        end
    else
        print("No path found.")
    end

    while true do
        wait(0) -- Additional logic can be added here to utilize the path within the game
    end
end
```
bool result, table colPoint = processLineOfSight(float originX, float originY, float originZ, float targetX, float targetY, float targetZ, [bool checkSolid=true], [bool car=false], [bool ped=false], [bool object=false], [bool particle=false], [bool seeThrough=false], [bool ignoreSomeObjects=false], [bool shotThrough=false])
Description
Looks for a touchpoint in three-dimensional space between two coordinates, along the direction of a straight line from origin to target. Has flags specifying which types of objects should be checked for collision.
Returns a table with information about the point of contact.

Arguments
float originX, float originY, float originZ initial position
float targetX, float targetY, float targetZ end position
bool checkSolid static objects (e.g. any buildings)
bool car transportation
bool ped characters
bool object dynamic objects
bool particle visual effects
bool seeThrough consider transparent objects
bool ignoreSomeObjects ignore some dynamic objects
bool shotThrough take into account objects that are shot through

Return values
bool result result of execution
table colPoint touch point information
^ Table structure ||
| float pos[1], float pos[2], float pos[3] | XYZ position of the touch point |
| float normal[1], float normal[2], float normal[3] | vector of normal to the contact surface |
| int surfaceType[1], int surfaceType[2] | surface types |
| int pieceType[1], int pieceType[2] | part types |
| float depth | some depth of something |
| int entity | a pointer to the object of the entity that collided |
| int entityType | the type of the entity |

With this feature, shoot rays out of the sky and the points of contact will be dijkstra points as well