Abstract: A method, computer readable medium, and system are disclosed for supersampling a large-scale and disjoined data set. The data set may include point cloud, voxel, or polygonal mesh data. The data set may be rendered using a distributed, sort-last rendering system that includes a plurality of rendering nodes and one or more compositing nodes. The method includes the steps of receiving graphics data at a plurality of rendering nodes, rendering at least a portion of the graphics data by one or more rendering nodes to produce multi-sample image data, encoding the multi-sample image data using a difference encoding technique, and transmitting the encoded multi-sample image data to a compositing node. The multi-sample image data comprises a plurality of values per pixel of a target image corresponding to a plurality of sample locations defined for each pixel of the target image.

Abstract: A method, computer readable medium, and system are disclosed for supersampling a large-scale and disjoined data set. The data set may include point cloud, voxel, or polygonal mesh data. The data set may be rendered using a distributed, sort-last rendering system that includes a plurality of rendering nodes and one or more compositing nodes. The method includes the steps of receiving graphics data at a plurality of rendering nodes, rendering at least a portion of the graphics data by one or more rendering nodes to produce multi-sample image data, encoding the multi-sample image data using a difference encoding technique, and transmitting the encoded multi-sample image data to a compositing node. The multi-sample image data comprises a plurality of values per pixel of a target image corresponding to a plurality of sample locations defined for each pixel of the target image.