32 citations · 37 across the 15 of their papers we have counts for
17 papers
PanoSeg3R: Feed-Forward 3D Semantic Segmentation for Panoramic Images with an Automatic Data Curation Pipeline
Heechan Yoon, Dongki Jung, Phuc Nguyen +2
We present PanoSeg3R, a feed-forward framework for 3D panoramic semantic segmentation. Unlike existing methods designed for perspective inputs, PanoSeg3R jointly predicts 3D geomet…
PanoPlane: Plane-Aware Panoramic Completion for Sparse-View Indoor 3D Gaussian Splatting
Adil Qureshi, Dongki Jung, Jaehoon Choi +1
We present PanoPlane, an approach for high-fidelity sparse-view indoor novel view synthesis that reconstructs closed room geometry via panoramic scene completion. Unlike perspectiv…
MiNER: A Two-Stage Pipeline for Metadata Extraction from Municipal Meeting Minutes
Rodrigo Batista, Luís Filipe Cunha, Purificação Silvano +4
Municipal meeting minutes are official documents of local governance, exhibiting heterogeneous formats and writing styles. Effective information retrieval (IR) requires identifying…
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…
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…
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…
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…
TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Rendering
Jaehoon Choi, Dongki Jung, Taejae Lee +4
We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone which offers access to images, depth maps, and valid poses. Our method first introduces…
SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning
Jaehoon Choi, Dongki Jung, Yonghan Lee +3
Monocular depth estimation in the wild inherently predicts depth up to an unknown scale. To resolve scale ambiguity issue, we present a learning algorithm that leverages monocular…
DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes
Dongki Jung, Jaehoon Choi, Yonghan Lee +4
We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments, e.g., a department store or a metro station. O…
SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments
Jaehoon Choi, Dongki Jung, Yonghan Lee +3
We present a novel algorithm for self-supervised monocular depth completion. Our approach is based on training a neural network that requires only sparse depth measurements and cor…
Arbitrary Style Transfer using Graph Instance Normalization
Dongki Jung, Seunghan Yang, Jaehoon Choi +1
Style transfer is the image synthesis task, which applies a style of one image to another while preserving the content. In statistical methods, the adaptive instance normalization…
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction
Jaehoon Choi, Dongki Jung, Donghwan Lee +1
Self-supervised monocular depth estimation has emerged as a promising method because it does not require groundtruth depth maps during training. As an alternative for the groundtru…
Partial Domain Adaptation Using Graph Convolutional Networks
Seunghan Yang, Youngeun Kim, Dongki Jung +1
Partial domain adaptation (PDA), in which we assume the target label space is included in the source label space, is a general version of standard domain adaptation. Since the targ…