Geometric compression through topological surgery
ACM Transactions on Graphics (TOG)
Differential and Numerically Invariant Signature Curves Applied to Object Recognition
International Journal of Computer Vision
QSplat: a multiresolution point rendering system for large meshes
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Numerically Invariant Signature Curves
International Journal of Computer Vision
Efficient high quality rendering of point sampled geometry
EGRW '02 Proceedings of the 13th Eurographics workshop on Rendering
Computing and Rendering Point Set Surfaces
IEEE Transactions on Visualization and Computer Graphics
Geometry-guided progressive lossless 3D mesh coding with octree (OT) decomposition
ACM SIGGRAPH 2005 Papers
Compression of dense and regular point clouds
AFRIGRAPH '06 Proceedings of the 4th international conference on Computer graphics, virtual reality, visualisation and interaction in Africa
Predictive point-cloud compression
SIGGRAPH '05 ACM SIGGRAPH 2005 Sketches
A Generic Scheme for Progressive Point Cloud Coding
IEEE Transactions on Visualization and Computer Graphics
One-shot Entire Shape Acquisition Method Using Multiple Projectors and Cameras
PSIVT '10 Proceedings of the 2010 Fourth Pacific-Rim Symposium on Image and Video Technology
Compression of 3-D Point Visual Data Using Vector Quantization and Rate-Distortion Optimization
IEEE Transactions on Multimedia
Octree-based point-cloud compression
SPBG'06 Proceedings of the 3rd Eurographics / IEEE VGTC conference on Point-Based Graphics
Hi-index | 0.00 |
Recently 3D scanning systems are capable of modeling entire dense shapes that evolve over time with a single scan (a.k.a. one-shot scan). In particular, structured-light-based systems have emerged as one-shot shape reconstruction methods that project a static grid pattern onto the object surface. This pattern allows the scanning of moving objects while still maintaining dense reconstruction. One-shot scanning systems are then capable of producing 3D+t (a.k.a. 4D) spatio-temporal models with millions of points. As a consequence, effective 4D geometry compression schemes are required to face the need to store or transmit the huge amount of data, in addition to classical static 3D data. In this paper, we propose a 4D spatiotemporal rate-distortion (RD) optimized point cloud encoder via a curve-based representation of the point cloud, particularly well-suited for one-shot scanning systems. The object surface is naturally sampled in a series of curves, due to the grid pattern. This motivates our choice to leverage a curve-based representation to remove the spatial and temporal correlation of the sampled point along the scanning directions through a competitive-based predictive encoder that includes different spatio-temporal prediction modes through an RD cost computation control. Experimental results show the significant gain obtained with the proposed method.