Eric M. Hoffert

dblp:12/2928 · DBLP profile ↗
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4ranked-venue papers
0as first author
0since 2021 · last 1989
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 4Human-computer interaction and ubiquitous computing · 3

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
3 papers
Rendering · 69% Geometric modeling and processing · 25% Computer animation and physical simulation · 6%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 100%

Topics — the 3 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Rendering
procedural texture synthesis
0.011989
Hypertexture · SIGGRAPH 1989
Geometric modeling and processing › shape modeling
volumetric modeling
0.011989
Hypertexture · SIGGRAPH 1989
Rendering
graphics pipeline
0.011987
FRAMES: Software tools for modeling, rendering and animation of 3D scenes · SIGGRAPH 1987

Methods — techniques the papers use, named apart from their topics

noise functions · 0.0density modulation · 0.0
YearPublicationVenuePosition
1989 A Parallel Image Computer with a Distributed Frame Buffer: System Architecture and Programming
abstract
We describe the system architecture and the programming environment of the Pixel Machine - a parallel image computer for 2D and 3D image synthesis and analysis. The architecture of the computer is based on an array of asynchronous MIMD nodes with a parallel access to a large frame buffer. The system consists of a pipeline of pipe nodes which execute sequential algorithms and an array of m x n pixel nodes which execute parallel algorithms. A pixel node accesses every m-th pixel on every n-th scan line of a distributed frame buffer. Each processing node is based on a high-speed, floating-point programmable processor. The programmability of the computer allows all algorithms to be implemented in software. A set of mapping functions transfers image algorithms written for conventional single-processor computers to algorithms which execute in the pixel nodes and access the distributed frame buffer. The ability to use floating-point computations in pixel operations, such as antialiasing, ray tracing, and filtering, allows high-quality image generation and processing. The image computer provides up to 820 megaflops of peak processing power and 48 megabytes of memory for data-visualization applications.
Michael Potmesil, Leonard McMillan, Eric M. Hoffert, Jennifer F. Inman, Robert L. Farah
Eurographics3
1989 Hypertexture
Ken Perlin, Eric M. Hoffert
SIGGRAPH2
1989 The pixel machine: a parallel image computer
abstract
We describe the system architecture and the programming environment of the Pixel Machine - a parallel image computer with a distributed frame buffer.The architecture of the computer is based on an array of asynchronous MIMD nodes with parallel access to a large frame buffer. The machine consists of a pipeline of pipe nodes which execute sequential algorithms and an array of m × n pixel nodes which execute parallel algorithms. A pixel node directly accesses every m-th pixel on every n-th scan line of an interleaved frame buffer. Each processing node is based on a high-speed, floating-point programmable processor.The programmability of the computer allows all algorithms to be implemented in software. We present the mappings of a number of geometry and image-computing algorithms onto the machine and analyze their performance.
Michael Potmesil, Eric M. Hoffert
SIGGRAPH2
1987 FRAMES: Software tools for modeling, rendering and animation of 3D scenes
abstract
FRAMES is a set of flexible software tools, developed for the UNIX programming environment, that can be used to generate images and animation of 3D scenes. In FRAMES, each stage of the image-rendering pipeline is assigned to a UNIX System filter. The following is a typical FRAMES pipe sequence where each filter performs a task implied by its name:cat scene.frm|euclid|mover|shade|camera|abufFRAMES was designed to be easy to use, to permit flexible experimentation with new ideas in image rendering and geometric modeling, to allow distribution of different parts of the rendering pipeline to different processors, and to specify images in a common format for display on a variety of devices.The user communicates with FRAMES via a command language. This language is extended whenever a software developer needs to incorporate a new idea into the system by adding new commands. Data flowing through the pipeline is modified by a collection of filter programs and passed through the pipe in text or binary format.The modular and pipe-based nature of FRAMES allows for multi/parallel processor implementations and device independence. FRAMES has generated images on a local-area network of minicomputers (each filter runs on a different processor) and on a 64-processor hypercube machine (one filter runs on 64 processors). Applications of FRAMES have ranged from reconstruction of neurons from serial sections to rendering of antialiased octree objects with subpixel detail.
Michael Potmesil, Eric M. Hoffert
SIGGRAPH2