[talks] C DeCoro preFPO

Melissa Lawson mml at CS.Princeton.EDU
Wed Aug 20 13:18:56 EDT 2008

Chris DeCoro will present his preFPO on Wednesday August 27 at 2PM in Room 402. 
The members of his committee are Szymon Rusinkiewicz (advisor);  Tom Funkhouser and Tim
(readers);  and Fei-Fei Li and Brian Kernighan (non-readers).  A copy of his abstract
below.  Everyone is invited to attend his talk.

Title: "Filters for Detail Control and Stylization in Rendering"

This dissertation examines a new class of filtering operations that are applied during
rendering -- the process of synthesizing an image from geometry.  These ``rendering
filters'' allow for detail control, creative stylization, and noise rejection in ways not
possible with purely image- or geometry-space filters.

In particular: the subtractive shadow filter enables flexible control of shadowing
level-of-detail in a real-time animation, allowing designers to rapidly prototype changes
to materials and geometry.  In our system, direct illumination is always computed and
preserved in full detail.  In contrast, the degree to which the perceptual cues provided
by shadowing are maintained is controlled by the user, as a tradeoff between accuracy and
rendering speed. Additionally, the subtractive shadow filter is able to take advantage of
the low-frequency content of shadows, relative to direct illumination, to improve
perceptual quality relative to conventional shadow computation with a limited number of

The stylized illumination filter gives the digital artist creative control over the
appearance of shadows and illumination, replicating many of the effects frequently
employed by traditional artists; In particular, we give the user control over the key
parameters of inflation, brightness, softness, and abstraction.  Because the filter
operates in real time, the artist may design the desired effect with interactive feedback.

The path-density filter allows for rejection of statistical outliers in the computation of
global illumination solutions, significantly decreasing noise and improving convergence.
The key insight of the method is to leverage density-estimation techniques in a
high-dimensional space representing scattering events along light paths; paths in
low-density regions are considered outliers. Unlike existing methods, it does so in a way
that is both robust to large-scale noise, while efficient on well-behaved distributions.

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