30 citations · 30 across the 4 of their papers we have counts for
7 papers
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…
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…