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Matthew Antone (MIT Graphics Group) tone@graphics.lcs.mit.edu
Scalable Extrinsic Registration of Omni-Directional Image
Networks
We describe linear-time algorithms for recovering scene-relative
camera orientations and positions in networks of thousands of
terrestrial images spanning hundreds of meters, in outdoor urban
scenes, under uncontrolled lighting. Accurate registration of
such image networks is currently infeasible by any other means,
manual or algorithmic. Our system requires no human input or
interaction, and recovers 6-DOF camera pose which is globally
consistent on average to roughly $0.1^{\circ}$ and five centimeters,
or about four pixels of epipolar alignment---sufficiently accurate
for applications such as 3D reconstruction and image-based rendering.
The 6-DOF registration problem is decoupled into pure rotation
and translation components, which take accurate intrinsic parameters,
approximate extrinsic pose, and a connected camera adjacency
graph as input. The algorithms estimate a local coordinate frame
at each camera by classifying and combining thousands of low-level
image features (lines and points) into a few robust aggregate
features (vanishing points and motion baselines). These local
frames are then propagated and registered through the adjacency
graph. As output, the algorithms produce an accurate assignment
of absolute orientation and position, and associated uncertainty
estimates, to every camera.
Our principal contributions include multi-camera probabilistic
extensions of classical two-camera alignment methods; new uses
of the Hough transform for initialization of iterative numerical
techniques; and formulation of expectation maximization algorithms
for the recovery of camera pose without explicit feature correspondence.
We also extend existing stochastic frameworks to handle unknown
numbers of 3-D feature points, unknown occlusion, large scale
(thousands of images and hundreds of thousands of features),
and large dimensional extent (inter-camera baselines of tens
of meters spanning areas hundreds of meters across). Finally,
we introduce principled methods for the estimation and propagation
of projective uncertainty, and present strong quantitative evidence
of the superior utility of wide-FOV images in extrinsic calibration.
We assess the system's performance on synthetic and real
data, and draw several conclusions. First, by fusing thousands
of gradient-based image features into a few ensemble projective
features, the algorithms achieve accurate registration even
in the face of significant lighting variations, low-level feature
noise, and error in initial pose estimates. Second, we show
that registration of wide-FOV images is fundamentally more robust
against failure, and more accurate, than is registration of
ordinary imagery. Finally, the system surmounts the usual tradeoff
between speed and precision: it is both faster and more accurate
than manual bundle adjustment.
(Joint work with Seth Teller).
Chandrajit
Bajaj (Department of Computer Sciences and Texas Institute
of Computational and Applied Mathematics (TICAM) The University
of Texas at Austin Austin, TX 78712-1188) bajaj@cs.utexas.edu
http://www.cs.utexas.edu/users/bajaj
http://www.ticam.utexas.edu
Time Critical Scalable Rendering of Massive Triangular
Meshes
Interactive browsing of massive triangular meshes is impossible
with current single desktop graphics workstations. In this talk
I shall describe a comprehensive approach, including the integration
of a new specialized piece of image compositing hardware called
the Metabuffer, in concert with back end compute + rendering
cluster of graphics workstations, to achieving scalable interactive
rendering. Our software solution is an end-to-end parallel and
progressive platform, from the initial data access to the final
display. I shall also provide performance details of our implemented
pipeline of parallel, and progressive image compositing solutions
coupled with parallel and progressive rendering of progressively
accessed triangular meshes.
David Banks
(Florida State University) banks@mailer.csit.fsu.edu
Theory of Rendering: Part 1
Rendering techniques borrow heavily from the physics of light
transport, which is ultimately explained by quantum electrodynamics
(QED). This talk bridges the first part of the gap, from QED
to Maxwell's equations, as part of a 4-part program to establish
a theoretical framework for rendering.
Baoquan
Chen (University of Minnesota)
POP:
A Hybrid Point and Polygon Rendering System for Large Data
We
introduce a simple but important extension to the existing
point rendering systems. Rather than using only points,
we use both points and polygons to represent and render
large meshe models. We start from triangles as leaf nodes
and build up a hierarchical tree structure with intermediate
nodes as points. During the rendering, the system determines
whether to use point (of certain level node) or triangle
(leaf node) for display depending on the screen contribution
of each node. While points are used to speedup the rendering
for distant objects, triangles are used to ensure the quality
for close objects. We also perform pre-texturing on points
and triangles when we build up the tree. Our hybrid representation
facilitates effective antialiasing for texture mapping.
Ronald Fedkiw
(Stanford Computer Science) fedkiw@cs.stanford.edu
Physics Based Modeling for Computer Graphics
Scientists and engineers have used numerical techniques to
simulate physical phenomena for many years. More recently, these
numerical techniques have worked their way into a variety of
new areas including computer graphics. Some key techniques will
be briefly discussed including the Level Set Method for tracking
interfaces and discontinuities, the Ghost Fluid Method for accurate
modeling of boundary conditions at these interfaces and discontinuities,
and Vorticity Confinement as a method of removing excess numerical
dissipation on coarse grids. More importantly, these techniques
will be shown "in action" for visual simulations of
smoke and water.
Hugues Hoppe
(Microsoft Research) http://research.microsoft.com/~hoppe/
Surface Parametrizations Slides:
html
pdf
This talk will present an overview of lapped textures and
its subsequent applications in real-time fur rendering and hatching.
Lapped textures cover an arbitrary surface by repeatedly instancing
a small example texture onto a set of overlapping surface patches.
The surface patches define local parametrizations that align
to a globally consistent direction field. Because they use a
small texture footprint, lapped textures reduce texture memory
and bandwidth. This memory savings is particularly important
when the texture is volumetric (for fur rendering), or when
it is tone-dependent (for hatching). For additional information,
refer to http://research.microsoft.com/~hoppe
.
Chris Johnson (University of Utah)
Computational Multi-Field Visualization
Computational field problems; such as computational fluid
dynamics (CFD), electromagnetic field simulation, and weather
modeling -- essentially any problems whose physics can be modeled
effectively by ordinary and/or partial differential equations--constitute
the majority of computational science and engineering simulations.
The output of such simulations might be a single field variable
(such as pressure or velocity) or a combination of fields involving
a number of scalar fields, vector fields, and/or tensor fields.
As such, scientific visualization researchers have concentrated
on effective ways to visualize large-scale computational fields.
Much current and previous visualization research has focused
on methods and techniques for visualizing a computational field
variables (such as the extraction of a single scalar field variable
as an isosurface). While single variable visualization often
satisfies the needs of the user, it is clear that it would also
be useful to be able to effectively visualize multiple fields
simultaneously.
In this talk I will describe some of our recent work in scalar,
vector, and tensor visualization techniques as applied to the
domain of computational field problems. I will end with a discussion
of ideas for the integration of techniques for creating computational
multi-field visualizations.
Leif Kobbelt (Computergraphik & Multimedia
RWTH-Aachen) kobbelt@cs.rwth-aachen.de
Feature Sensitive Mesh Processing
Many mesh processing algorithms assume the actual geometry
of a triangle mesh to be characterized by the vertex positions
only. From the manifold point of view however, triangle meshes
have to be considered as continuous piecewise linear surfaces.
In sufficiently smooth and flat regions of the surface this
observation doesn't really matter since any triangulation will
yield a decent approximation to the underlying geometry. In
the presence of sharply curved features however, this is not
true. Here, severe alias artifacts can affect the perceived
surface quality and can lead to quite bad approximation behavior.
In my talk I will discuss several consequences of this observation
and present recently developed algorithms for feature sensitive
mesh generation and re-meshing techniques. I will report recent
results in feature sensitive surface extraction from volume
data, surface anti-aliasing by remeshing of blend regions in
technical data sets, and diffusion based remeshing of triangle
meshes.
Jos Stam
(Alias/Wavefront) jstam@aw.sgi.com
Stable Fluids: Towards interactive visual simulations
of fluids
Building animation tools for fluid-like motions is an important
and challenging problem with many applications in computer graphics.
The use of physics-based models for fluid flow can greatly assist
in creating such tools. Physical models, unlike key frame or
procedural based techniques, permit an animator to almost effortlessly
create interesting, swirling fluid-like behaviors. Also, the
interaction of flows with objects and virtual forces is handled
elegantly. Until recently, it was believed that physical fluid
models were too expensive to allow real-time interaction. This
was largely due to the fact that previous models used unstable
schemes to solve the physical equations governing a fluid. In
this talk we propose an unconditionally stable model which still
produces complex fluid-like flows. As well, our method is very
easy to implement. The stability of our model allows us to take
larger time steps and therefore achieve faster simulations.
We have used our model in conjunction with advecting solid textures
to create many fluid-like animations interactively in two- and
three-dimensions.
Richard
Szeliski (Interactive Visual Media Group, Microsoft
Research) szeliski@microsoft.com
Representations for Image and Video-Based Modeling and
Rendering
Obtaining photo-realistic geometric and photometric models
is an important component of image-based rendering systems that
use real-world imagery as their input. Applications of such
systems include novel view generation and the mixing of live
imagery with synthetic computer graphics. In this talk, I review
a number of image-based representations (and their associated
reconstruction algorithms) we have developed in the last few
years. I begin by reviewing some recent approaches to the classic
problem of recovering a depth map from two or more images. I
then describe some of our newer representations and reconstruction
algorithms, including volumetric representations, layered plane-plus-parallax
representations (including the recovery of transparent and reflected
layers), and multiple depth maps. Each of these techniques has
its own strengths and weaknesses, which I will address. I will
also present our work in video-based rendering, in which we
synthesize novel video from short sample clips by discovering
their (quasi-repetive) temporal structure.
About the Speaker
Richard Szeliski is a Senior Researcher in the Vision Technology
Group at Microsoft Research, where he is pursuing research in
3-D computer vision, video scene analysis, and image-based rendering.
His current focus is on constructing photorealistic 3D scene
models from multiple images and video. He received a Ph.D. degree
in Computer Science from Carnegie Mellon University, Pittsburgh,
in 1988. He joined Microsoft Research in 1995. Prior to Microsoft,
he worked at Bell-Northern Research, Schlumberger Palo Alto
Research, the Artificial Intelligence Center of SRI International,
and the Cambridge Research Lab of Digital Equipment Corporation.
Dr. Szeliski has published over 80 research papers in computer
vision, computer graphics, medical imaging, and neural nets,
as well as the book Bayesian Modeling of Uncertainty in Low-Level
Vision. He is a Program Committee Chair for ICCV'2001, and is
on the Editorial Board of the International Journal of Computer
Vision. He has served as co-chair of the SPIE Conferences on
Geometric Methods in Computer Vision, the 1999 Vision Algorithms
Workshop, and as an Associate Editor of the IEEE Transactions
on Pattern Analysis and Machine Intelligence.
Gabriel Taubin
(IBM T.J.Watson Research Center and CalTech) taubin@caltech.edu http://mesh.caltech.edu/taubin
or taubin@us.ibm.com http://www.research.ibm.com/people/t/taubin
New Mesh Signal Processing Algorithms
Several closely related methods have been proposed in recent
years to smooth, denoise, edit, compress, transmit, and animate
very large polygonal models, based on signal processing techniques,
constrained energy minimization, and the solution of diffusion
differential equations. A number of these have been proposed
to fix some of the drawbacks of the simple Laplacian smoothing
algorithm, such as shrinkage, tangencial drift, and ridge over-smoothing.
Almost none of these extensions beat Laplacian smoothing in
simplicity and ease of implementation. In this talk I will describe
new signal processing algorithms, all based on various extension
and modifications of the discrete Laplacian operator defined
on a polygonal mesh. These new algorithms, solve some of the
existing problems while sharing the simplicity of Laplacian
smoothing.
Demetri Terzopoulos (Courant Institute-New
York University) dt@cs.nyu.edu
http://www.mrl.nyu.edu/~dt
Computational
Learning Techniques for Animation
We have been investigating the promising role that computational
theories of learning can play in the domain of computer graphics.
I will present the motivations and results of our research in
the context of physics and biology based modeling for animation.
In particular, the presentation will cover:
-
Efficient neural network emulators of physical dynamics, dubbed
"NeuroAnimators", that may be trained to produce physically
realistic motions by observing simulated physical systems
in action.
-
Biomechanically modeled artificial animals capable of acquiring,
through sensor-guided reinforcement learning, motor controllers
that effectuate lifelike, muscle-actuated locomotion.
-
Support vector machine learning methods to determine the functional
competencies of composable motor controllers for the physics-based
animation of articulated, anthropomorphic figures.
(References:
1. Grzeszczuk/Terzopoulos/Hinton SIGGRAPH 98. 2. Grzeszczuk/Terzopoulos SIGGRAPH 95.
3. Faloutsos/van de Panne/Terzopoulos SIGGRAPH 01.
See also Terzopoulos CACM 42(8):1999.)
Material
from Talks Computer
Graphics
2000-2001 Program: Mathematics in Multimedia
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