In computer graphics, marching squares is an algorithm that generates contours for a two-dimensional scalar field (rectangular array of individual numerical values). A similar method can be used to contour 2D triangle meshes. The contours can be of two kinds: Isolines – lines following a single data level, or isovalue. Isobands – filled areas between isolines. Typical applications include the contour lines on topographic maps or the generation of isobars for weather maps. Marching squares takes a similar approach to the 3D marching cubes algorithm: Process each cell in the grid independently. Calculate a cell index using comparisons of the contour level(s) with the data values at the cell corners. Use a pre-built lookup table, keyed on the cell index, to describe the output geometry for the cell. Apply linear interpolation along the boundaries of the cell to calculate the exact contour position. Basic algorithm Here are the steps of the algorithm: Apply a threshold to the 2D field to make a binary image containing: 1 where the data value is above the isovalue 0 where the data value is below the isovalue Every 2x2 block of pixels in the binary image forms a contouring cell, so the whole image is represented by a grid of such cells (shown in green in the picture below). Note that this contouring grid is one cell smaller in each direction than the original 2D field. For each cell in the contouring grid: Compose the 4 bits at the corners of the cell to build a binary index: walk around the cell in a clockwise direction appending the bit to the index, using bitwise OR and left-shift, from most significant bit at the top left, to least significant bit at the bottom left. The resulting 4-bit index can have 16 possible values in the range 0–15. Use the cell index to access a pre-built lookup table with 16 entries listing the edges needed to represent the cell (shown in the lower right part of the picture below). Apply linear interpolation between the original field data values to find the exact position of the contour line along the edges of the cell. Disambiguation of saddle points The contour is ambiguous at saddle points. It is possible to resolve the ambiguity by using the average data value for the center of the cell to choose between different connections of the interpolated points (four images in bottom-right