Dynamically reparameterized light fields

By Aaron Isaksen, Leonard McMillan, Steven J. Gortler. International Conference on Computer Graphics and Interactive Techniques, 2000

621 citations recorded by the source corpus.

This research further develops the light field and lumigraph image-based rendering methods and extends their utility. We present alternate parameterizations that permit 1) interactive rendering of moderately sampled light fields of scenes with significant, unknown depth variation and 2) low-cost, passive autostereoscopic viewing. Using a dynamic reparameterization, these techniques can be used to interactively render photographic effects such as variable focus and depth-of-field within a light field. The dynamic parameterization is independent of scene geometry and does not require actual or approximate geometry of the scene. We explore the frequency domain and ray-space aspects of dynamic reparameterization, and present an interactive rendering technique that takes advantage of today's commodity rendering hardware.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/bcefdaffb7f82b10a0f13cf29546bf4c6cef90d9

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Citations in the corpus

Plenoptic sampling (2000, background): “Similar sweeping effects have also been discussed in the dynamically reparameterized light field [13].”
Review of image-based rendering techniques (2000, background): “[14] proposed the notion of dynamically reparameterized light fields by adding the ability to vary the apparent focus within a light field using variable aperture and focus ring.”
Saliency Detection on Light Field (2017, positive): “[32] proposed to render DoF by reparameterizing the rays onto the focal plane and blending them via a wide aperture filter.”
Single lens 3D-camera with extended depth-of-field (2012, background): “Using equation (12), the image side DOF of the main lens can be approximated by γL( ) ≈ 2 pNL/ , where NL is the working f -number of the main lens.”
Using plane + parallax for calibrating dense camera arrays (2004, methodology): “Most light fields are acquired by a calibrated camera moving on a controlled gantry [ 7 , 8]. Recently, several researchers including ourselves have built large arrays of video cameras to capture light fields of dynamic scenes [14, 15].”
iLamps: geometrically aware and self-configuring projectors (2003, methodology): “We do this by synthetically refocusing [Isaksen et al. 2000] the array images to the foreground object’s depth plane.”
The Light Field Camera: Extended Depth of Field, Aliasing, and Superresolution (2012, methodology): “Stewart et al. [24] describe LF rendering methods to deal with such aliasing when these conditions are not met, based on combining band-limited [25] and wide-aperture [26] reconstructions.”
Spatio-angular resolution tradeoffs in integral photography (2006, methodology): “In this way, synthetic-aperture photography [LH96, IMG00 ] can compute photographs focused at different depths from a single light field by simple numerical integration over the desired aperture.”
Fourier slice photography (2005, background): “1996], but we have seen increasing interest in modeling a realistic camera with a lens that creates finite depth of field [Isaksen et al. 2000; Vaish et al. 2004; Levoy et al. 2004].”
High performance imaging using large camera arrays (2005, background): “Shifting the aligned images varies the focal depth for the system [Levoy and Hanrahan 1996; Isaksen et al. 2000; Vaish et al. 2004].”
Real-time consensus-based scene reconstruction using commodity graphics hardware (2002, background): “tion that allows the user to select a single depth value for all the view rays[12].”
Stereo for Image-Based Rendering using Image Over-Segmentation (2007, methodology): “A significantly denser camera configuration such as that of the Stanford Light Field Camera allows effects such as synthetic aperture and focusing (Isaksen et al., 2000).”
The focused plenoptic camera (2009, methodology): “Though these ideas have been developed and refined throughout history [2, 3, 11, 12], it wasn’t until we were able to apply digital processing that these techniques have become fully practical as a tool for general or creative photography [1, 7, 9, 13, 14, 17, 18].”
Light Field Image Processing: An Overview (2017, background): “The former has been addressed in seminal works on light field sampling [36], [254].”