Current Methods for Hair Modeling, Simulation, and Rendering

Current Methods for Hair Modeling, Simulation, and Rendering Abstract: Due to the extreme complexity of hair, its simulation continues to be an active research topic. Development of a realistic hair simulation requires solutions for modeling, physical simulation, and an accurate reflection model for rendering. The unique behavior of hair has lead researchers to question whether it should be handled as individual strands or a general volume of hairs and successful methods have been developed for both approaches. No technique has become the standard and the appropriateness of a solution may depend on its intended application, but as the computing power of hardware increases, the solutions for simulating realistic hair continue to improve.

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Image based 3D Reconstruction

IMAGE BASED MODELING ON A CONSTRAINED AXIS Abstract: Given a set of multiple calibrated photographs, silhouettes can be extracted to form a coarse approximation of 3D geometry. This image based model can further be refined by using feature based photoconsistency constraints and iterative bundle adjustment to correlate and refine the details found in the data set. Image based modeling has practical applications in visual effects, medical imaging, and prototyping.

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A COMPARISON OF TECHNIQUES FOR ESTIMATING DEPTH USING STEREO PAIRS

A COMPARISON OF TECHNIQUES FOR ESTIMATING DEPTH USING STEREO PAIRS Abstract: Disparity maps can provide a visual estimate of depth information generated by a pair of stereo images. The goal when producing disparity maps is to achieve a smooth and detailed map while minimizing error and maximizing efficiency. Three methods of varying complexity are explored to demonstrate and compare their results and efficiency on the Middlebury stereo test set [5].

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Ray Tracer: Basic Shading

Below are the results of my ray tracer with phong shading implemented. There is a white point light located at (0.1, 4.7, 0.0).

The same image with super sampling n=3

As an extra, I added multiple lights. Below is the image rendered with the original white light, a red light at (2, 5, -2), and a blue light at (-3, 2, 0).

The same image with super sampling n=3

Ray Tracer: Camera modeling

Below is the result of the basic casting. I adjusted some of the values of my previous assignment, but otherwise they are very similar.

As an extra, below is an image rendered with super sampling:

Project Proposal: Longest Common Subsequence in Parallel

The goal of the longest common subsequence (LCS) problem is to find the longest subsequence that is common to each member of a given set of sequences. LCS is useful for making comparisons and finding differences. The LCS prob- lem is NP-hard for an arbitrary number of inputs, but is O(n2) for two inputs, which is the problem we will solve, using an adaptation of the traditional dy- namic programming algorithm. Since a problem that can be solved by dynamic programming tends to easily be broken down into subproblems, the longest common subsequence problem will work well as a parallel program. Full Parallel Proposal (Pdf)

Ray Tracer: Setting the Scene

The final goal of the ray tracer will be to reproduce the image below.  In this first step, the object parameters are estimated.

Original Turner Whitted Image

Object Positions done in C++ and OpenGL

Initial Values:

Back Sphere Position: ( 1.0, 0.05, 3.65 ) Size: 1.0

Front Sphere Position: ( -0.15, .9, 0.8 ) Size: 1.0

Floor Position: ( -1.25, 0.0, 20.0 ) ( 4.6, 0.0, 20.0 ) ( 4.6, 0.0, 0.0 ) ( -1.25, 0.0, 0.0 )

Light Position: ( 1.0, 5.0, -1.0 )

Camera Position: ( 0.0, 1.0, -1.0 ) Camera Lookat: ( 0.0, 0.0, 20.0 ) Camera Orientation: ( 0.0, 1.0, 0.0 )

Multi touch Bar Counter (Video)

I came across this video while checking out what people were doing with multi touch screens and I think this is a pretty awesome use of the technology.  I like how occasionally neon lines will connect the objects toughing the table, even business cards!