Thouis "Ray" Jones 
Once upon a time, I worked in graphics and did graphics research, at MERL and in the MIT CSAIL Graphics Group. This page collects that work for reference purposes, but in general I no longer work in these areas or keep up on new results, except in the area of Poissondisk pattern generation.
LinearTime PoissonDisk Patterns (Preprint)
Journal of Graphics, GPU, and Game Tools, 2011, Vol. 15, Issue 3, pp. 177182. Thouis Jones, David R. Karger We present an algorithm for generating Poissondisk patterns taking O(N) time to generate N points. The method is based on a grid of regions that can contain no more than one point in the final pattern, and which uses an explicit model of pointarrival times under a uniform Poisson process.  

 
Efficient Generation of PoissonDisk Sampling Patterns (Preprint)
Journal of Graphics Tools, 2006, Vol. 11, Issue 2, pp. 2736. Thouis Jones Poisson Disk sampling patterns are of interest to the graphics community because their bluenoise properties are desirable in sampling patterns for rendering, illumination, and other computations. Until now, such patterns could only be generated by slow methods with poor convergence, or could only be approximated by jittering individual samples or tiling sets of samples. We present a simple and efficient randomized algorithm for generating true Poisson Disk sampling patterns in a square domain, given a minimum radius R between samples. The algorithm runs in O(N logN) time for a pattern of N points. The method is based on the Voronoi diagram. Source code is available online. See also the work by Daniel Dunbar and Greg Humphreys to appear in SIGGRAPH 2006.  

 
Normal Improvement for Point Rendering
to appear in IEEE Computer Graphics & Applications, July/August 2004, pages 5356. Thouis Jones, Frédo Durand, Matthias Zwicker Point models from scanned data invariably contain noise. Most denoising methods concentrate on positional information rather than normals, even though rendered images are affected more strongly by noise in normals than positions. We propose a novel method for denoising normals for point models, based on the bilateral filter. We treat the filter as a spatial deformation and update normals iteratively. The bilateral filter is featurepreserving; our extension to normals inherits this trait. The source code for this paper was written as a PointShop3D plugin. It can be downloaded here. It may or may not work with the latest version of PointShop3D. I have not tested it recently. However, I tried to keep the code as clean as possible, so that it could be used as a reference for other implementations.  

 
Interpolation Search for NonIndependent Data in Proceedings of the 15th Annual ACMSIAM Symposium on Discrete Algorithms (SODA 2004), January 2004, pages 823832. Erik D. Demaine, Thouis Jones, Mihai Patrascu
We define a deterministic metric of "wellbehaved data" that enables
searching along the lines of interpolation search.
Specifically, define $\Delta$ to be the ratio of distances between
the farthest and nearest pair of adjacent elements.
We develop a data structure that stores a dynamic set of $n$ integers
subject to insertions, deletions, and predecessor/successor queries
in $O(\lg \Delta)$ time per operation.
This result generalizes interpolation search and interpolation search trees
smoothly to nonrandom (in particular, nonindependent) input data.
In this sense, we capture the amount of "pseudorandomness"
required for effective interpolation search.  
NonIterative, FeaturePreserving Mesh Smoothing (Slides, Didactic demo) SIGGRAPH 2003, pp. 943949 Thouis R. Jones, Frédo Durand, Mathieu Desbrun With the increasing use of geometry scanners to create 3D models, there is a rising need for fast and robust mesh smoothing to remove inevitable noise in the measurements. While most previous work has favored diffusionbased iterative techniques for featurepreserving smoothing, we propose a radically different approach, based on robust statistics and local firstorder predictors of the surface. The robustness of our local estimates allows us to derive a noniterative featurepreserving filtering technique applicable to arbitrary "triangle soups". We demonstrate its simplicity of implementation and its efficiency, which make it an excellent solution for smoothing large, noisy, and nonmanifold meshes. SOURCE CODE AND MESHES (NEW VERSION WITH BUGFIX.) 
 
ExampleBased SuperResolution Computer Graphics and Applications 22(2), March 2002, pp. 5665 William T. Freeman, Thouis R. Jones, Egon C. Pasztor Imagebased models for computer graphics lack resolution independence: they cannot be zoomed much beyond the pixel resolution they were sampled at without a degradation of quality. Interpolating images usually results in a blurring of edges and image details. We describe image interpolation algorithms which use a database of training images to create plausible highfrequency details in zoomed images. Image preprocessing steps allow the use of image detail from regions of the training images which may look quite different from the image to be processed. These methods preserve fine details, such as edges, generate believable textures, and can give good results even after zooming multiple octaves. 
 
Adaptively Sampled Distance Fields: A General Representation of Shape for Computer Graphics SIGGRAPH 2000, pp. 249254 Sarah F. Frisken, Ronald N. Perry, Alyn P. Rockwood, Thouis R. Jones Adaptively Sampled Distance Fields (ADFs) are a unifying representation of shape that integrate numerous concepts in computer graphics including the representation of geometry and volume data and a broad range of processing operations such as rendering, sculpting, levelofdetail management, surface offsetting, collision detection, and color gamut correction. Its structure is uncomplicated and direct, but is especially effective for quality reconstruction of complex shapes, e.g., artistic and organic forms, precision parts, volumes, high order functions, and fractals. We characterize one implementation of ADFs, illustrating its utility on two diverse applications: 1) artistic carving of fine detail, and 2) representing and rendering volume data and volumetric effects. Other applications are briefly presented. 
 
Antialiasing with Line Samples Rendering Techniques '00 (Proceedings of the 11th Eurographics Workshop on Rendering), pp. 197205 Thouis R. Jones, Ronald N. Perry Antialiasing is a necessary component of any high quality renderer. An antialiased image is produced by convolving the scene with an antialiasing filter and sampling the result, or equivalently by solving the antialiasing integral at each pixel. Though methods for analytically computing this integral exist, they require the continuous twodimensional result of visiblesurface computations. Because these computations are expensive, most renderers use supersampling, a discontinuous approximation to the integral. We present a new algorithm, line sampling, combining a continuous approximation to the integral with a simple visiblesurface algorithm. Line sampling provides high quality antialiasing at significantly lower cost than analytic methods while avoiding the visual artifacts caused by supersampling's discontinuous nature. A line sample is a line segment in the image plane, centered at a pixel and spanning the footprint of the antialiasing filter. The segment is intersected with scene polygons, visible subsegments are determined, and the antialiasing integral is computed with those subsegments and a onedimensional reparameterization of the integral. On simple scenes where edge directions can be precomputed, one correctly oriented line sample per pixel suffices for antialiasing. Complex scenes can be antialiased by combining multiple line samples weighted according to the orientation of the edges they intersect. 
