The vertex encoding algorithms can be used to compress arbitrary streams of attribute data; one other use case besides triangle meshes is point cloud data. Typically point clouds come with position, color and possibly other attributes but don't have an implied point order.

To compress point clouds efficiently, it's recommended to first preprocess the points by sorting them using the spatial sort algorithm:

std::vector<unsigned int> remap(point_count);
meshopt_spatialSortRemap(&remap[0], positions, point_count, sizeof(vec3));

// for each attribute stream
meshopt_remapVertexBuffer(positions, positions, point_count, sizeof(vec3), &remap[0]);

After this the resulting arrays should be quantized (e.g. using 16-bit fixed point numbers for positions and 8-bit color components), and the result can be compressed using meshopt_encodeVertexBuffer as described in the previous section. To decompress, meshopt_decodeVertexBuffer will recover the quantized data that can be used directly or converted back to original floating-point data. The compression ratio depends on the nature of source data, for colored points it's typical to get 35-40 bits per point as a result.