8 citations · 21 across the 20 of their papers we have counts for
11 papers · 1 filter
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…