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Christopher Uchytil

dblp:281/9843 · also Chris Uchytil · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0002-3870-4666ORCID · reported

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

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

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
1 paper
Geometric modeling and processing · 77% Computational fabrication · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing › implicit surface
function representation
0.812024
A Function-Based Approach to Interactive High-Precision Volumetric Design and Fabrication · ACM Trans. Graph. 2024

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

interval arithmetic · 0.8JIT compilation · 0.8GPU parallelism · 0.8
YearPublicationVenuePosition
2024 A Function-Based Approach to Interactive High-Precision Volumetric Design and Fabrication
abstract
We present a novel function representation (F-Rep) based geometric modeling kernel tailor-made to support computer aided design (CAD) and fabrication of high resolution volumetric models containing hundreds of billions of voxel grid elements. Our modeling kernel addresses existing limitations associated with evaluating, storing, and accessing volumetric data produced by F-Reps in contexts outside of rendering. The result is an F-Rep modeling kernel well suited for CAD-based applications. Our kernel utilizes a sparse volume data structure to manage F-Rep data while efficient F-Rep evaluation is achieved through a combination of interval arithmetic (IA), just-in-time (JIT) compilation of user-defined functions, and massively parallel evaluation on the GPU. We employ IA as the basis for local pruning of the function evaluation tree to minimize total function evaluations, we use a novel JIT compilation scheme to optimize function execution, and we take advantage of GPU-parallelism to enhance computational throughput. We illustrate the kernel’s effectiveness in visualizing and slicing models with complex defining functions and detailed geometry, and utilize the geometry kernel to manufacture a physical part. Additionally, we present performance metrics across multiple hardware configurations demonstrating significant performance improvements over existing F-Rep geometry kernels, and we examine how our geometry kernel scales with computing power.
Christopher Uchytil, Duane W. Storti
ACM Trans. Graph.1