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We introduce QuadStack, a novel algorithm for volumetric data compression and direct rendering. Our algorithm exploits the data redundancy often found in layered datasets which are common in science and engineering fields such as geology, biology, mechanical engineering, medicine, etc. QuadStack first compresses the volumetric data into vertical stacks which are then compressed into a quadtree that identifies and represents the layered structures at the internal nodes. The associated data (color, material, density, etc.) and shape of these layer structures are decoupled and encoded independently, leading to high compression rates (4× to 54× of the original voxel model memory footprint in our experiments). We also introduce an algorithm for value retrieving from the QuadStack representation and we show that the access has logarithmic complexity. Because of the fast access, QuadStack is suitable for efficient data representation and direct rendering. We show that our GPU implementation performs comparably in speed with the state-of-the-art algorithms (18-79 MRays/s in our implementation), while maintaining a significantly smaller memory footprint. [ABSTRACT FROM AUTHOR]

The flexible pinhole camera (FPC) allows flexible modulation of the sampling rate over the field of view. The FPC is defined by a viewpoint and a map specifying the sampling locations on the image plane. The map is constructed from known regions of interest with interactive and automatic approaches. The FPC provides inexpensive 3D projection that allows rendering complex datasets quickly, in feed-forward fashion, by projection followed by rasterization. The FPC supports many types of data, including image, height field, geometry, and volume data. The resulting image is a coherent nonuniform sampling (CoNUS) of the dataset that matches the local variation of the dataset's importance. CoNUS images have been successfully implemented for remote visualization, focus-plus-context visualization, and acceleration of expensive rendering effects such as surface geometric detail and specular reflection. A video explaining and demonstrating the FPC is at http://youtu.be/kvFe5XjOPNM. [ABSTRACT FROM AUTHOR]

This issue marks the start of a new volume (volume 39) of I Computer Graphics Forum i (CGF). We are very glad about this development and thank all CGF authors, the reviewers and associate editors, the paper chairs of CGF-associated conferences, and our colleagues at Wiley for strengthening this journal. Victor Ostromoukhov, Université de Lyon 1, CNRS-LIRIS. [Extracted from the article]

Computer graphics research is increasingly interested in the high-level analysis and processing of geometric objects. By acquiring a structural or functional understanding of 3D shapes, researchers are able to tackle mid- to high-level design problems for which machine computations can replace or at least relieve human efforts. In parallel, with the rapid advances in 3D printing technologies, many design solutions explored by researchers and practitioners are focusing on the needs and constraints arising from physical fabrication. The contributions in this special issue are cross-disciplinary, connecting physical fabrication with design and processing tasks in new domains including circuit design, geospatial visualization, and 3D scanning, leading to never-before-seen 3D printing applications. [ABSTRACT FROM PUBLISHER]