Automatic viewpoint selection algorithms try to optimize the view of a data set to best show its features. They are often based on information theoretic frameworks. Although many algorithms have shown useful results, they often take several seconds to produce a result because they render the scene from a variety of viewpoints and analyze the result. In this article, we propose a new algorithm for volume data sets that dramatically reduces the running time. Our entire algorithm takes less than a second, which allows it to be integrated into real-time volume-rendering applications. The interactive performance is achieved by solving a maximization problem with a small sample of the data set, instead of rendering it from a variety of directions. We compare performance results of our algorithm to state-of-the-art approaches and show that our algorithm achieves comparable results for the resulting viewpoints. Furthermore, we apply our algorithm to multichannel volume data sets.
VázquezP-PFeixasMSbertM. Viewpoint selection using viewpoint entropy. In: Proceedings of the vision modeling and visualization conference 2001, VMV ’01, pp. 273–280. Aka GmbH, 2001Stuttegart, Germany.
2.
BordoloiUDShenH-W. View selection for volume rendering. Visualization, 2005. VIS 05. IEEE, vol., no., pp. 487–494, 23–28 Oct. 2005 doi: 10.1109/VISUAL.2005.1532833.
3.
SbertMPlemenosDFeixasM. Viewpoint quality: measures and applications. In: Eurographics workshop on computational aesthetics in graphics, visualization and imaging (eds NeumannLSbertMGoochB.), Girona, Spain; 18–20 May, 2005. pp. 185–192.
4.
TakahashiSFujishiroITakeshimaY. A feature-driven approach to locating optimal viewpoints for volume visualization. Visual Conf IEEE2005; pp. 495–502.
5.
JiGShenH-W. Dynamic view selection for time-varying volumes. IEEE T Vis Comput Gr2006; 12: 1109–1116.
6.
TaoYLinHBaoH. Structure-aware viewpoint selection for volume visualization. In: IEEE Pacific 2009, visualization symposium, Bejing, China; April 20–23; pp. 193–200.
7.
VázquezP-PSbertM. Fast adaptive selection of best views. In: Proceedings of the 2003 international conference on computational science and its applications: part III, ICCSA’03, Montreal Quebec, Canada; May 18–21 May; pp. 295–305. Berlin, Heidelberg: Springer-Verlag, 2003.
8.
LeeCHVarshneyAJacobsDW. Mesh saliency. In: ACM SIGGRAPH 2005 Papers, SIGGRAPH ’05, Los Angeles, CA; July 31st - August 4th; pp. 659–666. New York: ACM, 2005.
9.
HarrisCStephensM. A. combined corner and edge detector. In: Proceedings of the 4th Alvey vision conference, August 31st-September 2nd; 1988, pp. 147–151; http://www.bmva.org/bmvc/1988/avc-88-000.pdf.
10.
KnissJKindlmannGHansenC. Interactive volume rendering using multi-dimensional transfer functions | direct manipulation widgets. In: VIS ’01: proceedings of the conference on visualization ’01, San Diego, CA, USA; October 21–26 pp. 255–262. Washington, DC: IEEE Computer Society, 2001.
11.
KimHSUnatDBadenSB. Interactive data-centric viewpoint selection. Visual Data Anal2012; 8294(5): 829405.
12.
BlanzVTarrMJBlthoffHH. What object attributes determine canonical views?1999 Perception; 28: 575–600.
13.
BowyerKWDyerCR. Aspect graphs: an introduction and survey of recent results. Int J Imag Syst Tech1990; 2(4): 315–328.
14.
KoenderinkJJDoornAJ. The singularities of the visual mapping. Biol Cybern1976; 24: 51–59.
15.
KoenderinkJJDoornAJ. The internal representation of solid shape with respect to vision. Biol Cybern1979; 32: 211–216.
16.
PalmerSRoschEChaseP. Canonical perspective and the perception of objects. Attention Perform1981; IX: 131–151.
17.
PolonskyOPatanGBiasottiS. What is an image? Towards the computation of the “best” view of an object. Visual Comput2005; 21(8–10): 840–847.
SipiranIBustosB. A robust 3d interest points detector based on Harris operator. In 3DOR. 2010, pp. 7–14.
20.
PearsonK On lines and planes of closest fit to systems of points in space. Philos Mag1901; 2(6): 559–572.
21.
BoydSVandenbergheL. Convex optimization. New York: Cambridge University Press, 2004.
22.
ForsythDAPonceJ. Computer vision: a modern approach. Prentice Hall, New Jersey, 2002.
23.
UnatDCaiXBadenSB. Mint: realizing CUDA performance in 3D stencil methods with Annotated C. In: Proceedings of the international conference on supercomputing, ICS ’11, Tucson, AZMay 31st-June 4th, 2011, pp. 214–224. New York: ACM, 2011.
24.
NickollsJBuckIGarlandM. Scalable parallel programming with CUDA. Queue2008; 6: 40–53.
25.
UnatDKimHSSchulzeJP. Auto-optimization of a feature selection algorithm. Proceedings of the 4th workshop on emerging applications and many-core architecture, San Jose, CA. June 4th 2011.
26.
DagumLMenonR. OpenMP: an industry standard API for shared-memory programming. IEEE Comput Sci Eng1998; 5(1): 46–55.
27.
RogerDAssarssonUHolzschuchN. Efficient stream reduction on the GPU. Workshop on general purpose processing on graphics processing units, Boston, MA, October 2007.
28.
NukadaAOgataYEndoT. Bandwidth intensive 3-d fft kernel for GPUS using CUDA. International conference for high performance computing, networking, storage and analysis, 2008. Austin, Texas, SC 2008. 2008, pp. 1–11.
ParisSKornprobstPTumblinJ. Bilateral filtering. Hanover, MA: Now Publishers Inc., 2009.
31.
ParisSDurandF. A fast approximation of the bilateral filter using a signal processing approach. Int J Comput Vision2009; 81: 24–52.
32.
MartoneMEGuptaAWongM. A cell-centered database for electron tomographic data. J Struct Biol2002; 138: 145–155.
33.
MikulaSTrottsIStoneJM. Internet-enabled high-resolution brain mapping and virtual microscopy. Neuroimage35(1): 9–15.
34.
LoweDG. Object recognition from local scale-invariant features. In: Proceedings of the international conference on computer vision-Volume 2, ICCV ’99, Kerkyra Greece, September 20–27, p.1150. Washington, DC: IEEE Computer Society, 1999.
