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researcher

S. Shin

5 papers hereh-index 388 citations7 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author2

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.GR1
same name
  • S. Shin — 21 papers, h 25
  • S. Shin — 8 papers, h 2
  • S. Shin — 7 papers, h 18
  • S. Shin — 4 papers, h 4
  • S. Shin — 3 papers, h 4
  • S. Shin — 3 papers, h 8

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedLocality-aware Gaussian Compression for Fast and High-quality Rendering

3 citations · 3 across the 4 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

Explicit Layer Modeling for Video Object Insertion and Layer Decomposition

Kyujin Han, Seungjoo Shin, Sunghyun Cho

Most video editing systems still lack explicit layered video representations, limiting their ability to perform realistic compositing, object reuse, and consistent manipulation. Th…

cs.CV2025

Leveraging Learned Image Prior for 3D Gaussian Compression

Seungjoo Shin, Jaesik Park, Sunghyun Cho

Compression techniques for 3D Gaussian Splatting (3DGS) have recently achieved considerable success in minimizing storage overhead for 3D Gaussians while preserving high rendering…

cs.CV2025★ 3 cited

Locality-aware Gaussian Compression for Fast and High-quality Rendering

Seungjoo Shin, Jaesik Park, Sunghyun Cho

We present LocoGS, a locality-aware 3D Gaussian Splatting (3DGS) framework that exploits the spatial coherence of 3D Gaussians for compact modeling of volumetric scenes. To this en…

cs.CV2023

Binary Radiance Fields

Seungjoo Shin, Jaesik Park

In this paper, we propose \textit{binary radiance fields} (BiRF), a storage-efficient radiance field representation employing binary feature encoding that encodes local features us…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.