Temporal light field reconstruction for rendering distribution effects

Lehtinen, Jaakko; Aila, Timo; Chen, Jiawen; Laine, Samuli; Durand, Frédo

doi:10.1145/1964921.1964950

Cited by 35 publications

(41 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hachisuka et al [2008] introduced optimized adaptive sampling strategies for light field generation using raytracing. Lehtinen et al [2011] exploit preceding light field analysis to efficiently and adaptively sample and reconstruct multi-dimensional signals in ray-tracing. Our light field analysis differs as our goal is to determine the best angular and spatial resolution trade-off.…”

Section: Related Workmentioning

confidence: 99%

Content-adaptive lenticular prints

et al. 2013

View full text Add to dashboard Cite

Lenticular prints are a popular medium for producing automultiscopic glasses-free 3D images. The light field emitted by such prints has a fixed spatial and angular resolution. We increase both perceived angular and spatial resolution by modifying the lenslet array to better match the content of a given light field. Our optimization algorithm analyzes the input light field and computes an optimal lenslet size, shape, and arrangement that best matches the input light field given a set of output parameters. The resulting emitted light field shows higher detail and smoother motion parallax compared to fixed-size lens arrays. We demonstrate our technique using rendered simulations and by 3D printing lens arrays, and we validate our approach in simulation with a user study.

show abstract

Section: Related Workmentioning

confidence: 99%

Content-adaptive lenticular prints

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Recently, Lehtinen et al [2011] proposed a reconstruction method for motion blur and depth of field from sparse sampling of the 5D light field that uses speed and depth information to reproject samples into each pixel in order to obtain an approximate dense sampling. This method neglects the variation a sample might have along its reprojection line which might lead to artifacts when reconstructing glossy surfaces.…”

Section: Sparse and Low-rank Approximations Of Light Transportmentioning

confidence: 99%

5D Covariance tracing for efficient defocus and motion blur

et al. 2013

View full text Add to dashboard Cite

The rendering of effects such as motion blur and depth-of-field requires costly 5D integrals. We dramatically accelerate their computation through adaptive sampling and reconstruction based on the prediction of the anisotropy and bandwidth of the integrand. For this, we develop a new frequency analysis of the 5D temporal light-field, and show that first-order motion can be handled through simple changes of coordinates in 5D. We further introduce a compact representation of the spectrum using the covariance matrix and Gaussian approximations. We derive update equations for the 5 × 5 covariance matrices for each atomic light transport event, such as transport, occlusion, BRDF, texture, lens, and motion. The focus on atomic operations makes our work general, and removes the need for special-case formulas. We present a new rendering algorithm that computes 5D covariance matrices on the image plane by tracing paths through the scene, focusing on the single-bounce case. This allows us to reduce sampling rates when appropriate and perform reconstruction of images with complex depth-of-field and motion blur effects.

show abstract

“…Sampling in Graphics has a long tradition [Cook et al 1984] and is a standard tool in rendering global illumination effects [Veach and Guibas 1997;Lehtinen et al 2012], shadows [Egan et al 2011] as well as depth of field and motion blur [Soler et al 2009;Egan et al 2009;Lehtinen et al 2011;Li et al 2012]. Our algorithm uses a simple Markov chain to select new sample positions; more sophisticated sampling strategies, such as the above, could improve performance.…”

Section: Related Workmentioning

confidence: 99%

Adaptive image synthesis for compressive displays

et al. 2013

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

Temporal light field reconstruction for rendering distribution effects

Cited by 35 publications

References 24 publications

Content-adaptive lenticular prints

Content-adaptive lenticular prints

5D Covariance tracing for efficient defocus and motion blur

Adaptive image synthesis for compressive displays

Contact Info

Product

Resources

About