Search Results for author: Hyeonwoo Yu

Found 7 papers, 3 papers with code

RGBD GS-ICP SLAM

2 code implementations19 Mar 2024 Seongbo Ha, Jiung Yeon, Hyeonwoo Yu

Simultaneous Localization and Mapping (SLAM) with dense representation plays a key role in robotics, Virtual Reality (VR), and Augmented Reality (AR) applications.

Simultaneous Localization and Mapping

Self-supervised Learning of 3D Object Understanding by Data Association and Landmark Estimation for Image Sequence

no code implementations14 Apr 2021 Hyeonwoo Yu, Jean Oh

Therefore, we propose a strategy to exploit multipleobservations of the object in the image sequence in orderto surpass the self-performance: first, the landmarks for theglobal object map are estimated through network predic-tion and data association, and the corrected annotation fora single frame is obtained.

Object Pose Estimation +1

Domain Adaptive Monocular Depth Estimation With Semantic Information

no code implementations12 Apr 2021 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.

Image Classification Monocular Depth Estimation

Anchor Distance for 3D Multi-Object Distance Estimation from 2D Single Shot

no code implementations25 Jan 2021 Hyeonwoo Yu, Jean Oh

Given a 2D Bounding Box (BBox) and object parameters, a 3D distance to the object can be calculated directly using 3D reprojection; however, such methods are prone to significant errors because an error from the 2D detection can be amplified in 3D.

Autonomous Driving Object +4

Anytime 3D Object Reconstruction using Multi-modal Variational Autoencoder

no code implementations25 Jan 2021 Hyeonwoo Yu, Jean Oh

In this context, we propose a method for imputation of latent variables whose elements are partially lost.

3D Object Reconstruction 3D Shape Reconstruction +4

Zero-shot Learning via Simultaneous Generating and Learning

1 code implementation NeurIPS 2019 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 descriptions for both seen and unseen classes.

Zero-Shot Learning

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