Stochastic tomography and its applications in 3D imaging of mixing fluids

Gregson, James; Krimerman, Michael; Hullin, Matthias B.; Heidrich, Wolfgang

doi:10.1145/2185520.2335403

Cited by 23 publications

(39 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Table 1, the average generation time is about 4.5 s in Mixture dataset with resolution 310 × 200, and is only 2.4 s in Candle dataset with resolution 240 × 200. The total time of flame reconstruction is no more than 5 s per frame, which is much less than the flame sheet decomposition (hours) [3] and the algebraic tomographic reconstruction algorithm (minutes or dozens of minutes) [13,28]. In order to describe the performance clearly, we converted the Table 1 data into 3-layer pie chart representing the results of each dataset (Candle, Alcohol and Mixture).…”

Section: Resultsmentioning

confidence: 99%

Reconstruction of three-dimensional flame with color temperature

Zhou

Tian

et al. 2014

Vis Comput

View full text Add to dashboard Cite

The reconstruction of flame from the captured images is a difficult and computationally expensive problem. Reconstruction from color images will keep the colorful appearance, as is beneficial for visually realistic flame modeling. Most of existing color-image-based methods rebuild three density fields from RGB intensities; however, these methods suffer from the color distortion problem due to the high correlation of RGB intensities. A novel method for 3D flame reconstruction using color temperature is presented in this paper. Color-temperature mapping is calculated to avoid color distortion; this method maps the RGB intensities into the color temperature and its joint intensity. We improve the multiplication reconstruction with visual hull restriction so that the energy distribution is more reasonable, which allows avoidance of the impossible zones. Experimental results indicate that our approach is efficient in the visually plausible 3D flame generation and produces better color restorations.

show abstract

Section: Resultsmentioning

confidence: 99%

Reconstruction of three-dimensional flame with color temperature

Zhou

Tian

et al. 2014

Vis Comput

View full text Add to dashboard Cite

show abstract

“…The inverse problem could in principle be solved using 1 minimization algorithms [4]. Recent grid-free methods [11] may also provide a method to solve this problem efficiently. In contrast, if scattering is taken into account, every voxel potentially influences the intensity of every image pixel, and the inherent sparsity of the projection operator M is lost.…”

Section: Discussionmentioning

confidence: 99%

Visualization of Astronomical Nebulae via Distributed Multi-GPU Compressed Sensing Tomography

Wenger

Ament

Guthe

et al. 2012

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

(a) (b) (c) (d) Fig. 1. The planetary nebula M2-9 is a typical example of a bipolar nebula. Its quasi-symmetric twin lobes of ionized material emanate from a binary star system in its center. Assuming axial symmetry, our reconstruction algorithm uses a single input image (a) to produce a high-resolution 3D visualization that closely resembles the original image when rendered from the same viewpoint (b). From a novel vantage point, the emission along the principal axis of the nebula accumulates and creates a luminous halo (c). As the vantage point approaches the symmetry axis, the received intensity further increases and the perceived shape of the nebula changes toward two entangled rings (d). The resolution of the reconstructed volume is 512 3 voxels. Original image: Bruce Balick (University of Washington), Vincent Icke (Leiden University, The Netherlands), Garrelt Mellema (Stockholm University), and NASA.Abstract-The 3D visualization of astronomical nebulae is a challenging problem since only a single 2D projection is observable from our fixed vantage point on Earth. We attempt to generate plausible and realistic looking volumetric visualizations via a tomographic approach that exploits the spherical or axial symmetry prevalent in some relevant types of nebulae. Different types of symmetry can be implemented by using different randomized distributions of virtual cameras. Our approach is based on an iterative compressed sensing reconstruction algorithm that we extend with support for position-dependent volumetric regularization and linear equality constraints. We present a distributed multi-GPU implementation that is capable of reconstructing high-resolution datasets from arbitrary projections. Its robustness and scalability are demonstrated for astronomical imagery from the Hubble Space Telescope. The resulting volumetric data is visualized using direct volume rendering. Compared to previous approaches, our method preserves a much higher amount of detail and visual variety in the 3D visualization, especially for objects with only approximate symmetry.

show abstract

“…Most recently, stochastic tomography has been proposed for the application of capturing mixing fluids [Gregson et al 2012]. Their method follows a traditional pipeline approach, where all observations are measured first and then the full-sized inverse problem is solved with the help of sampling techniques.…”

Section: Related Workmentioning

confidence: 99%

Adaptive image synthesis for compressive displays

et al. 2013

Self Cite

View full text Add to dashboard Cite

Figure 1: Adaptive light field synthesis for a dual-layer compressive display. By combining sampling, rendering, and display-specific optimization into a single framework, the proposed algorithm facilitates light field synthesis with significantly reduced computational resources. Redundancy in the light field as well as limitations of display hardware are exploited to generate high-quality reconstructions (center left column) for a high-resolution target light field of 85 × 21 views with 840 × 525 pixels each (center). Our adaptive reconstruction uses only 3.82% of the rays in the full target light field (left column), thus providing significant savings both during rendering and during the computation of the display parameters. The proposed framework allows for higher-resolution light fields, better 3D effects, and perceptually correct animations to be presented on emerging compressive displays (right columns). AbstractRecent years have seen proposals for exciting new computational display technologies that are compressive in the sense that they generate high resolution images or light fields with relatively few display parameters. Image synthesis for these types of displays involves two major tasks: sampling and rendering high-dimensional target imagery, such as light fields or time-varying light fields, as well as optimizing the display parameters to provide a good approximation of the target content.In this paper, we introduce an adaptive optimization framework for compressive displays that generates high quality images and light fields using only a fraction of the total plenoptic samples. We demonstrate the framework for a large set of display technologies, including several types of auto-stereoscopic displays, high dynamic range displays, and high-resolution displays. We achieve significant performance gains, and in some cases are able to process data that would be infeasible with existing methods.

show abstract

Stochastic tomography and its applications in 3D imaging of mixing fluids

Cited by 23 publications

References 0 publications

Reconstruction of three-dimensional flame with color temperature

Reconstruction of three-dimensional flame with color temperature

Visualization of Astronomical Nebulae via Distributed Multi-GPU Compressed Sensing Tomography

Adaptive image synthesis for compressive displays

Contact Info

Product

Resources

About