Three Dimensional Distance Calculator
Our free coordinate geometry calculator solves three dimensional distance problems. Get worked examples, visual aids, and downloadable results.
Reviewed by Manoj Kumar, Mathematics Educator
Formula
d = sqrt((x2-x1)^2 + (y2-y1)^2 + (z2-z1)^2)
The 3D distance formula extends the Pythagorean theorem to three dimensions, calculating the straight-line (Euclidean) distance between two points in space.
Worked Examples
Example 1: Distance Between Two Points in 3D
Problem:Find the distance between points A(1, 2, 3) and B(7, 10, 5).
Solution:dx = 7 - 1 = 6\ndy = 10 - 2 = 8\ndz = 5 - 3 = 2\nd = sqrt(6^2 + 8^2 + 2^2) = sqrt(36 + 64 + 4) = sqrt(104) = 10.198\nMidpoint = ((1+7)/2, (2+10)/2, (3+5)/2) = (4, 6, 4)\nManhattan distance = |6| + |8| + |2| = 16
Result:3D Distance = 10.198 | Midpoint = (4, 6, 4) | Manhattan = 16
Example 2: Aircraft Separation Distance
Problem:Two aircraft are at positions (100, 200, 35000) and (400, 600, 37000) in feet. Find their separation.
Solution:dx = 400 - 100 = 300 ft\ndy = 600 - 200 = 400 ft\ndz = 37000 - 35000 = 2000 ft\nd = sqrt(300^2 + 400^2 + 2000^2)\n = sqrt(90000 + 160000 + 4000000)\n = sqrt(4250000) = 2061.55 ft\nHorizontal separation = sqrt(300^2 + 400^2) = 500 ft\nVertical separation = 2000 ft
Result:3D Distance = 2061.55 ft | Horizontal = 500 ft | Vertical = 2000 ft
Frequently Asked Questions
What is the three-dimensional distance formula and how is it derived?
The 3D distance formula calculates the straight-line distance between two points in three-dimensional space. Given points P1(x1, y1, z1) and P2(x2, y2, z2), the distance equals sqrt((x2-x1)^2 + (y2-y1)^2 + (z2-z1)^2). This formula is derived by applying the Pythagorean theorem twice: first to find the distance in the xy-plane, then combining that result with the z-component difference. The formula is a natural extension of the 2D distance formula and belongs to the family of Euclidean distance metrics. It represents the shortest possible path between two points in flat three-dimensional space.
How does 3D distance differ from 2D distance calculations?
The 3D distance formula adds a third squared difference term (z2-z1)^2 under the square root compared to the 2D version. In 2D space, distance is calculated as sqrt((x2-x1)^2 + (y2-y1)^2), which only considers positions on a flat plane. Adding the z-axis introduces depth, allowing measurement of distances through three-dimensional space such as between aircraft at different altitudes, between floors in a building, or between atoms in a molecule. The 3D distance is always greater than or equal to any of the projected 2D distances (XY, XZ, or YZ planes), with equality only when the points differ in exactly two coordinates.
What are Manhattan and Chebyshev distances in three dimensions?
Manhattan distance (also called taxicab or L1 distance) sums the absolute differences along each axis: |x2-x1| + |y2-y1| + |z2-z1|. It represents the distance traveled when movement is restricted to axis-parallel directions, like navigating a city grid in 3D. Chebyshev distance (also called chessboard or L-infinity distance) takes the maximum of the absolute differences: max(|x2-x1|, |y2-y1|, |z2-z1|). It represents the minimum number of moves a king would need on a 3D chessboard. Manhattan distance is always greater than or equal to Euclidean distance, while Chebyshev distance is always less than or equal. Both are valid distance metrics used in different applications.
How is 3D distance used in computer graphics and game development?
Three-dimensional distance calculations are fundamental to computer graphics and game engines. They are used for collision detection between objects, determining whether a player is within range of an item or enemy, calculating lighting intensity based on distance from a light source (inverse square law), rendering depth-of-field effects, and implementing fog or atmospheric attenuation. Games often use the squared distance (without the square root) for performance optimization when only relative distances matter, since the square root operation is computationally expensive. Spatial partitioning structures like octrees and BVH trees reduce the number of distance calculations needed by organizing objects hierarchically.
References
Reviewed by Manoj Kumar, Mathematics Educator · Editorial policy