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Giancarlo Pereira

dblp:378/2376 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
0000-0002-5869-6886ORCID · 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 2021Human-computer interaction and ubiquitous computing · 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
Computational photography and imaging · 44% Geometric modeling and processing · 44% Computer animation and physical simulation · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Geometric modeling and processing
3d reconstruction
0.912025
LookUp3D: Data-Driven 3D Scanning · SIGGRAPH Asia 2025
Computational photography and imaging
3d scanning
0.912025
LookUp3D: Data-Driven 3D Scanning · SIGGRAPH Asia 2025

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

time-of-flight · 0.9structured light · 0.9stereo · 0.9calibration · 0.9
YearPublicationVenuePosition
2025 LookUp3D: Data-Driven 3D Scanning
abstract
High speed, high-resolution, and accurate 3D scanning would open doors to many new applications in graphics, robotics, science, and medicine by enabling the accurate scanning of deformable objects during interactions. Past attempts to use structured light, time-of-flight, and stereo in high-speed settings have usually required tradeoffs in resolution or inaccuracy. In this paper, we introduce a method that enables, for the first time, 3D scanning at 450 frames per second at 1 Megapixel, or 1,450 frames per second at 0.4 Megapixel in an environment with controlled lighting. The key idea is to use a per-pixel lookup table that maps colors to depths, which is built using a linear stage. Imperfections, such as lens-distortion and sensor defects are baked into the calibration. We describe our method and test it on a novel hardware prototype. We compare the system with both ground-truth geometry as well as commercially available dynamic sensors like the Microsoft Kinect and Intel Realsense. Our results show the system acquiring geometry of objects undergoing high-speed deformations and oscillations and demonstrate the ability to recover physical properties from the reconstructions.
Giancarlo Pereira, Yidan Gao, Yurii Piadyk, David F. Fouhey, Cláudio T. Silva, Daniele Panozzo
SIGGRAPH Asia1