27 citations · 42 across the 13 of their papers we have counts for
14 papers
NoDrift3R: Raymap-Guided Coupling for Drift-Robust Unposed Feed-Forward 3D Reconstruction
Xiangyu Sun, Liu Liu, Seungkwon Yang +4
Pose-Free Feed-forward 3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for fast scene reconstruction. However, its performance degrades significantly in lo…
2Xplat: Decoupling Geometry and Appearance Modeling for Feed-Forward 3D Gaussian Splatting
Hwasik Jeong, Seungryong Lee, Gyeongjin Kang +4
Pose-free feed-forward 3D Gaussian Splatting (3DGS) has opened a new frontier for rapid 3D modeling, enabling high-quality Gaussian representations to be generated from uncalibrate…
Multi-view Pyramid Transformer: Look Coarser to See Broader
Gyeongjin Kang, Seungkwon Yang, Seungtae Nam +3
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 sing…
Uni3R: Unified 3D Reconstruction and Semantic Understanding via Generalizable Gaussian Splatting from Unposed Multi-View Images
Xiangyu Sun, Haoyi Jiang, Liu Liu +8
Reconstructing and semantically interpreting 3D scenes from sparse 2D views remains a fundamental challenge in computer vision. Conventional methods often decouple semantic underst…
iLRM: An Iterative Large 3D Reconstruction Model
Gyeongjin Kang, Seungtae Nam, Seungkwon Yang +4
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 3…
Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction
Seungtae Nam, Xiangyu Sun, Gyeongjin Kang +3
Generalized feed-forward Gaussian models have achieved significant progress in sparse-view 3D reconstruction by leveraging prior knowledge from large multi-view datasets. However,…
SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splatting
Gyeongjin Kang, Jisang Yoo, Jihyeon Park +5
We propose SelfSplat, a novel 3D Gaussian Splatting model designed to perform pose-free and 3D prior-free generalizable 3D reconstruction from unposed multi-view images. These sett…
Freq-Mip-AA : Frequency Mip Representation for Anti-Aliasing Neural Radiance Fields
Youngin Park, Seungtae Nam, Cheul-hee Hahm +1
Neural Radiance Fields (NeRF) have shown remarkable success in representing 3D scenes and generating novel views. However, they often struggle with aliasing artifacts, especially w…
Mip-Grid: Anti-aliased Grid Representations for Neural Radiance Fields
Seungtae Nam, Daniel Rho, Jong Hwan Ko +1
Despite the remarkable achievements of neural radiance fields (NeRF) in representing 3D scenes and generating novel view images, the aliasing issue, rendering "jaggies" or "blurry"…
Coordinate-Aware Modulation for Neural Fields
Joo Chan Lee, Daniel Rho, Seungtae Nam +2
Neural fields, mapping low-dimensional input coordinates to corresponding signals, have shown promising results in representing various signals. Numerous methodologies have been pr…
Separable Physics-Informed Neural Networks
Junwoo Cho, Seungtae Nam, Hyunmo Yang +3
Physics-informed neural networks (PINNs) have recently emerged as promising data-driven PDE solvers showing encouraging results on various PDEs. However, there is a fundamental lim…
Masked Wavelet Representation for Compact Neural Radiance Fields
Daniel Rho, Byeonghyeon Lee, Seungtae Nam +3
Neural radiance fields (NeRF) have demonstrated the potential of coordinate-based neural representation (neural fields or implicit neural representation) in neural rendering. Howev…
Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks
Junwoo Cho, Seungtae Nam, Hyunmo Yang +3
Physics-informed neural networks (PINNs) have emerged as new data-driven PDE solvers for both forward and inverse problems. While promising, the expensive computational costs to ob…
Streamable Neural Fields
Junwoo Cho, Seungtae Nam, Daniel Rho +2
Neural fields have emerged as a new data representation paradigm and have shown remarkable success in various signal representations. Since they preserve signals in their network p…