TY - GEN
T1 - DuoDecim
T2 - Eurographics/IEEE VGTC Symposium on Point-Based Graphics, 2005
AU - Krüger, Jens
AU - Schneider, Jens
AU - Westermann, Rüdiger
PY - 2005
Y1 - 2005
N2 - In this paper we present a compression scheme for large point scans including per-point normals. For the encoding of such scans we introduce a particular type of closest sphere packing grids, the hexagonal close packing (HCP). HCP grids provide a structure for an optimal packing of 3D space, and for a given sampling error they result in a minimal number of cells if geometry is sampled into these grids. To compress the data, we extract linear sequences (runs) of filled cells in HCP grids. The problem of determining optimal runs is turned into a graph theoretical one. Point positions and normals in these runs are incrementally encoded. At a grid spacing close to the point sampling distance, the compression scheme only requires slightly more than 3 bits per point position. Incrementally encoded per-point normals are quantized at high fidelity using only 5 bits per normal (see Figure 1). The compressed data stream can be decoded in the graphics processing unit (GPU). Decoded point positions are saved in graphics memory, and they are then used on the GPU again to render point primitives. In this way we render gigantic point scans from their compressed representation in local GPU memory at interactive frame rates (see Figure 2).
AB - In this paper we present a compression scheme for large point scans including per-point normals. For the encoding of such scans we introduce a particular type of closest sphere packing grids, the hexagonal close packing (HCP). HCP grids provide a structure for an optimal packing of 3D space, and for a given sampling error they result in a minimal number of cells if geometry is sampled into these grids. To compress the data, we extract linear sequences (runs) of filled cells in HCP grids. The problem of determining optimal runs is turned into a graph theoretical one. Point positions and normals in these runs are incrementally encoded. At a grid spacing close to the point sampling distance, the compression scheme only requires slightly more than 3 bits per point position. Incrementally encoded per-point normals are quantized at high fidelity using only 5 bits per normal (see Figure 1). The compressed data stream can be decoded in the graphics processing unit (GPU). Decoded point positions are saved in graphics memory, and they are then used on the GPU again to render point primitives. In this way we render gigantic point scans from their compressed representation in local GPU memory at interactive frame rates (see Figure 2).
UR - http://www.scopus.com/inward/record.url?scp=33745263017&partnerID=8YFLogxK
U2 - 10.1109/pbg.2005.194070
DO - 10.1109/pbg.2005.194070
M3 - Conference contribution
AN - SCOPUS:33745263017
SN - 3905673207
SN - 9783905673203
T3 - Point-Based Graphics, 2005 - Eurographics/IEEE VGTC Symposium Proceedings
SP - 99
EP - 107
BT - Point-Based Graphics, 2005 - Eurographics/IEEE VGTC Symposium Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 June 2005 through 21 June 2005
ER -