30 citations · 34 across the 10 of their papers we have counts for
10 papers
Just Flip: Flipped Observation Generation and Optimization for Neural Radiance Fields to Cover Unobserved View
Minjae Lee, Kyeongsu Kang, Hyeonwoo Yu
With the advent of Neural Radiance Field (NeRF), representing 3D scenes through multiple observations has shown remarkable improvements in performance. Since this cutting-edge tech…
Necessity Feature Correspondence Estimation for Large-scale Global Place Recognition and Relocalization
Kyeongsu Kang, Minjae Lee, Hyeonwoo Yu
Global place recognition and 3D relocalization are one of the most important components in the loop closing detection for 3D LiDAR Simultaneous Localization and Mapping (SLAM). In…
Self-supervised Learning of 3D Object Understanding by Data Association and Landmark Estimation for Image Sequence
Hyeonwoo Yu, Jean Oh
In this paper, we propose a self-supervised learningmethod for multi-object pose estimation. 3D object under-standing from 2D image is a challenging task that infers ad-ditional di…
Domain Adaptive Monocular Depth Estimation With Semantic Information
Fei Lu, Hyeonwoo Yu, Jean Oh
The advent of deep learning has brought an impressive advance to monocular depth estimation, e.g., supervised monocular depth estimation has been thoroughly investigated. However,…
Anchor Distance for 3D Multi-Object Distance Estimation from 2D Single Shot
Hyeonwoo Yu, Jean Oh
Visual perception of the objects in a 3D environment is a key to successful performance in autonomous driving and simultaneous localization and mapping (SLAM). In this paper, we pr…
Anytime 3D Object Reconstruction using Multi-modal Variational Autoencoder
Hyeonwoo Yu, Jean Oh
For effective human-robot teaming, it is important for the robots to be able to share their visual perception with the human operators. In a harsh remote collaboration setting, dat…
Zero-shot Learning via Simultaneous Generating and Learning
Hyeonwoo Yu, Beomhee Lee
To overcome the absence of training data for unseen classes, conventional zero-shot learning approaches mainly train their model on seen datapoints and leverage the semantic descri…
Not Only Look But Observe: Variational Observation Model of Scene-Level 3D Multi-Object Understanding for Probabilistic SLAM
Hyeonwoo Yu
We present NOLBO, a variational observation model estimation for 3D multi-object from 2D single shot. Previous probabilistic instance-level understandings mainly consider the singl…
A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM
H. W. Yu, B. H. Le
We present a Bayesian object observation model for complete probabilistic semantic SLAM. Recent studies on object detection and feature extraction have become important for scene u…
A Variational Feature Encoding Method of 3D Object for Probabilistic Semantic SLAM
H. W. Yu, B. H. Lee
This paper presents a feature encoding method of complex 3D objects for high-level semantic features. Recent approaches to object recognition methods become important for semantic…