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Seoha Kim

4 accepted papers

2026

4D Scaffold Gaussian Splatting with Dynamic-Aware Anchor Growing for Efficient and High-Fidelity Dynamic Scene Reconstruction

AAAI 2026technical

Modeling dynamic scenes through 4D Gaussians offers high visual fidelity and fast rendering speeds, but comes with significant storage overhead. Recent approaches mitigate this cost by aggressively reducing the number of Gaussians. However, this inevitably removes Gaussians essential for high-qualit

Cited by 10SourcePDFScholar
2025

Rethinking Open-Vocabulary Segmentation of Radiance Fields in 3D Space

AAAI 2025technical

Understanding the 3D semantics of a scene is a fundamental problem for various scenarios such as embodied agents. While NeRFs and 3DGS excel at novel-view synthesis, previous methods for understanding their semantics have been limited to incomplete 3D understanding: their segmentation results are re…

Cited by 2SourcePDFScholar
2024

Per-Gaussian Embedding-Based Deformation for Deformable 3D Gaussian Splatting

ECCV 2024poster

"As 3D Gaussian Splatting (3DGS) provides fast and high-quality novel view synthesis, it is a natural extension to deform a canonical 3DGS to multiple frames for representing a dynamic scene. However, previous works fail to accurately reconstruct complex dynamic scenes. We attribute the failure to t…

2024

Sync-NeRF: Generalizing Dynamic NeRFs to Unsynchronized Videos

AAAI 2024technical

Recent advancements in 4D scene reconstruction using neural radiance fields (NeRF) have demonstrated the ability to represent dynamic scenes from multi-view videos. However, they fail to reconstruct the dynamic scenes and struggle to fit even the training views in unsynchronized settings. It happens…