2 citations · 3 across the 10 of their papers we have counts for
12 papers
SyncTrack4D: Cross-Video Motion Alignment and Video Synchronization for Multi-Video 4D Gaussian Splatting
Yonghan Lee, Tsung-Wei Huang, Shiv Gehlot +3
Modeling dynamic 3D scenes is challenging due to their high-dimensional nature, which requires aggregating information from multiple views to reconstruct time-evolving 3D geometry…
MoRe: Monocular Geometry Refinement via Graph Optimization for Cross-View Consistency
Dongki Jung, Jaehoon Choi, Yonghan Lee +3
Monocular 3D foundation models offer an extensible solution for perception tasks, making them attractive for broader 3D vision applications. In this paper, we propose MoRe, a train…
Splatblox: Traversability-Aware Gaussian Splatting for Outdoor Robot Navigation
Samarth Chopra, Jing Liang, Gershom Seneviratne +5
We present Splatblox, a real-time system for autonomous navigation in outdoor environments with dense vegetation, irregular obstacles, and complex terrain. Our method fuses segment…
RPG360: Robust 360 Depth Estimation with Perspective Foundation Models and Graph Optimization
Dongki Jung, Jaehoon Choi, Yonghan Lee +1
The increasing use of 360 images across various domains has emphasized the need for robust depth estimation techniques tailored for omnidirectional images. However, obtaining large…
UAV4D: Dynamic Neural Rendering of Human-Centric UAV Imagery using Gaussian Splatting
Jaehoon Choi, Dongki Jung, Christopher Maxey +4
Despite significant advancements in dynamic neural rendering, existing methods fail to address the unique challenges posed by UAV-captured scenarios, particularly those involving m…
SpeeDe3DGS: Speedy Deformable 3D Gaussian Splatting with Temporal Pruning and Motion Grouping
Allen Tu, Haiyang Ying, Alex Hanson +3
Dynamic extensions of 3D Gaussian Splatting (3DGS) achieve high-quality reconstructions through neural motion fields, but per-Gaussian neural inference makes these models computati…
UAVTwin: Neural Digital Twins for UAVs using Gaussian Splatting
Jaehoon Choi, Dongki Jung, Yonghan Lee +3
We present UAVTwin, a method for creating digital twins from real-world environments and facilitating data augmentation for training downstream models embedded in unmanned aerial v…
EDM: Equirectangular Projection-Oriented Dense Kernelized Feature Matching
Dongki Jung, Jaehoon Choi, Yonghan Lee +4
We introduce the first learning-based dense matching algorithm, termed Equirectangular Projection-Oriented Dense Kernelized Feature Matching (EDM), specifically designed for omnidi…
IM360: Large-scale Indoor Mapping with 360 Cameras
Dongki Jung, Jaehoon Choi, Yonghan Lee +1
We present a novel 3D mapping pipeline for large-scale indoor environments. To address the significant challenges in large-scale indoor scenes, such as prevalent occlusions and tex…
MeshGS: Adaptive Mesh-Aligned Gaussian Splatting for High-Quality Rendering
Jaehoon Choi, Yonghan Lee, Hyungtae Lee +2
Recently, 3D Gaussian splatting has gained attention for its capability to generate high-fidelity rendering results. At the same time, most applications such as games, animation, a…
Mode-GS: Monocular Depth Guided Anchored 3D Gaussian Splatting for Robust Ground-View Scene Rendering
Yonghan Lee, Jaehoon Choi, Dongki Jung +4
We present a novel-view rendering algorithm, Mode-GS, for ground-robot trajectory datasets. Our approach is based on using anchored Gaussian splats, which are designed to overcome…
TK-Planes: Tiered K-Planes with High Dimensional Feature Vectors for Dynamic UAV-based Scenes
Christopher Maxey, Jaehoon Choi, Yonghan Lee +3
In this paper, we present a new approach to bridge the domain gap between synthetic and real-world data for unmanned aerial vehicle (UAV)-based perception. Our formulation is desig…