Seungkwon Yang — accepted AI-conference papers

2026

Multi-view Pyramid Transformer: Look Coarser to See Broader

Gyeongjin Kang, Seungkwon Yang, Seungtae Nam, Younggeun Lee, Jungwoo Kim, Eunbyung Park

CVPR 2026

We propose Multi-view Pyramid Transformer (MVP), a scalable multi-view transformer architecture that directly reconstructs large 3D scenes from tens to hundreds of images in a single forward pass. Drawing on the idea of "looking broader to see the whole, looking finer to see the details," MVP is bui

Cited by 0 Source ↗code ↗Scholar ↗

2026

iLRM: An Iterative Large 3D Reconstruction Model

Gyeongjin Kang, Seungtae Nam, Seungkwon Yang, Xiangyu Sun, Sameh Khamis, Abdelrahman Mohamed +1

CVPR 2026

Feed-forward 3D modeling has emerged as a promising approach for rapid and high-quality 3D reconstruction. In particular, directly generating explicit 3D representations, such as 3D Gaussian splatting, has attracted significant attention due to its fast and high-quality rendering. However, many stat

Cited by 0 Source ↗code ↗Scholar ↗