The Quantized Kd-Tree: Compression of Huge Point Sampled Models
Authors: Erik Hubo, Tom Mertens and Philippe Bekaert
Type: Technical Sketch
Conference Name: SIGGRAPH
Conference Location: Boston, MA
Date: 29 July - 03 Aug 2006
The ever-increasing demand for more geometric detail persists.
These days, complex models can easily be obtained through
the acquisition of real-world objects using 3D scanning devices.
The sheer number of surface elements in these scanned datasets
advocates the use of a point-based representation.
The conceptual simplicity of points scales well for large
datasets and has emerged as a viable alternative over traditional
primitives such as triangles and parametric surfaces. The
main goal in this sketch is to compress these huge sampled
models, possibly consisting of tens to hundreds of millions
Even though using points eases storage requirements (e.g.
by not storing connectivity information), compression is likely
to be required for real-world applications. A scene of 80M
points for instance, already requires 1GB of memory just for
storing 3D positions at 32 bit precision, which does not even
include normals and colors. Compression enables us to render
such and even larger objects without requiring expensive disk