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Seonghyuk Hong

dblp:237/2177 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 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.

Artificial intelligence
1 paper
3D vision · 100%
Computer graphics and multimedia
1 paper
Rendering · 100%

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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › 3d reconstruction
multimodal 3d reconstruction
0.912025
InsideOut: Integrated RGB-Radiative Gaussian Splatting for Comprehensive 3D Object Representation · ICCV 2025
Computer vision › 3D vision
object representation
0.912025
InsideOut: Integrated RGB-Radiative Gaussian Splatting for Comprehensive 3D Object Representation · ICCV 2025
Rendering
gaussian splatting
0.912025
InsideOut: Integrated RGB-Radiative Gaussian Splatting for Comprehensive 3D Object Representation · ICCV 2025

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

x-ray reference loss · 1.7hierarchical fitting · 1.7
YearPublicationVenuePosition
2025 InsideOut: Integrated RGB-Radiative Gaussian Splatting for Comprehensive 3D Object Representation
abstract
We introduce InsideOut, an extension of 3D Gaussian splatting (3DGS) that bridges the gap between high-fidelity RGB surface details and subsurface X-ray structures. The fusion of RGB and X-ray imaging is invaluable in fields such as medical diagnostics, cultural heritage restoration, and manufacturing. We collect new paired RGB and X-ray data, perform hierarchical fitting to align RGB and X-ray radiative Gaussian splats, and propose an X-ray reference loss to ensure consistent internal structures. InsideOut effectively addresses the challenges posed by disparate data representations between the two modalities and limited paired datasets. This approach significantly extends the applicability of 3DGS, enhancing visualization, simulation, and non-destructive testing capabilities across various domains.
Seonghyuk Hong, Jaeyoon Lee
ICCV2