8 citations · 21 across the 20 of their papers we have counts for
23 papers
DeltaSplat: Iterative Gaussian Refinement for Pose-Free Feed-Forward 3D Gaussian Splatting
Chanung Park, Seunghyeon Song, Joo Chan Lee +2
Pose-free feed-forward 3D Gaussian Splatting (3DGS) reconstructs a scene from sparse, unposed images in a single network pass, removing the need for camera calibration and per-scen…
ACA-GS: Adaptive-Capacity Anchored Gaussian Splatting for Compact Dynamic Radiance Fields
Seunghyeon Song, Joo Chan Lee, Chanung Park +4
Recent advances in 4D Gaussian Splatting (4DGS) enable high-fidelity, real-time spatiotemporal rendering, but expose a fundamental trade-off between motion expressiveness and stora…
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…
Scenes as Objects, Not Primitives: Instance-Structured 3D Tokenization from Unposed Views
Mijin Yoo, In Cho, Subin Jeon +3
A 3D scene is understood through its objects, not the primitives that compose them. Yet feed-forward reconstruction methods output dense, unstructured sets of points or Gaussians,…
ILV: Iterative Latent Volumes for Fast and Accurate Sparse-View CT Reconstruction
Seungryong Lee, Woojeong Baek, Joosang Lee +1
A long-term goal in CT imaging is to achieve fast and accurate 3D reconstruction from sparse-view projections, thereby reducing radiation exposure, lowering system cost, and enabli…
Optimized Minimal 4D Gaussian Splatting
Minseo Lee, Byeonghyeon Lee, Lucas Yunkyu Lee +7
4D Gaussian Splatting has emerged as a new paradigm for dynamic scene representation, enabling real-time rendering of scenes with complex motions. However, it faces a major challen…
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…
Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation
Seungjun Oh, Younggeun Lee, Hyejin Jeon +1
Recent advancements in dynamic 3D scene reconstruction have shown promising results, enabling high-fidelity 3D novel view synthesis with improved temporal consistency. Among these,…
Optimized Minimal 3D Gaussian Splatting
Joo Chan Lee, Jong Hwan Ko, Eunbyung Park
3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time, high-performance rendering, enabling a wide range of applications. However, representing 3D sce…
EditSplat: Multi-View Fusion and Attention-Guided Optimization for View-Consistent 3D Scene Editing with 3D Gaussian Splatting
Dong In Lee, Hyeongcheol Park, Jiyoung Seo +6
Recent advancements in 3D editing have highlighted the potential of text-driven methods in real-time, user-friendly AR/VR applications. However, current methods rely on 2D diffusio…
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…
Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields
Joo Chan Lee, Daniel Rho, Xiangyu Sun +2
3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussian-based representation and introduces an approximated volumetric rende…
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…
F-3DGS: Factorized Coordinates and Representations for 3D Gaussian Splatting
Xiangyu Sun, Joo Chan Lee, Daniel Rho +3
The neural radiance field (NeRF) has made significant strides in representing 3D scenes and synthesizing novel views. Despite its advancements, the high computational costs of NeRF…
Separable Physics-informed Neural Networks for Solving the BGK Model of the Boltzmann Equation
Jaemin Oh, Seung Yeon Cho, Seok-Bae Yun +2
In this study, we introduce a method based on Separable Physics-Informed Neural Networks (SPINNs) for effectively solving the BGK model of the Boltzmann equation. While the mesh-fr…
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"…
Continuous Memory Representation for Anomaly Detection
Joo Chan Lee, Taejune Kim, Eunbyung Park +2
There have been significant advancements in anomaly detection in an unsupervised manner, where only normal images are available for training. Several recent methods aim to detect a…
Deblurring 3D Gaussian Splatting
Byeonghyeon Lee, Howoong Lee, Xiangyu Sun +2
Recent studies in Radiance Fields have paved the robust way for novel view synthesis with their photorealistic rendering quality. Nevertheless, they usually employ neural networks…
Sharp-NeRF: Grid-based Fast Deblurring Neural Radiance Fields Using Sharpness Prior
Byeonghyeon Lee, Howoong Lee, Usman Ali +1
Neural Radiance Fields (NeRF) have shown remarkable performance in neural rendering-based novel view synthesis. However, NeRF suffers from severe visual quality degradation when th…
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…
Compact 3D Gaussian Representation for Radiance Field
Joo Chan Lee, Daniel Rho, Xiangyu Sun +2
Neural Radiance Fields (NeRFs) have demonstrated remarkable potential in capturing complex 3D scenes with high fidelity. However, one persistent challenge that hinders the widespre…