Search Results for author: Jaehoon Choi

Found 12 papers, 3 papers with code

SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning

no code implementations10 Mar 2022 Jaehoon Choi, Dongki Jung, Yonghan Lee, Deokhwa Kim, Dinesh Manocha, Donghwan Lee

Given these metric poses and monocular sequences, we propose a self-supervised learning method for the pre-trained supervised monocular depth networks to enable metrically scaled depth estimation.

Monocular Depth Estimation Robot Navigation +2

DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes

no code implementations ICCV 2021 Dongki Jung, Jaehoon Choi, Yonghan Lee, Deokhwa Kim, Changick Kim, Dinesh Manocha, Donghwan Lee

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.

3D Reconstruction Depth Estimation

Just a Few Points Are All You Need for Multi-View Stereo: A Novel Semi-Supervised Learning Method for Multi-View Stereo

no code implementations ICCV 2021 Taekyung Kim, Jaehoon Choi, Seokeon Choi, Dongki Jung, Changick Kim

We generate the spare ground truth of the DTU dataset for evaluation and extensive experiments verify that our SGT-MVSNet outperforms the state-of-the-art MVS methods on the sparse ground truth setting.

3D Reconstruction

SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction

1 code implementation6 Oct 2020 Jaehoon Choi, Dongki Jung, Donghwan Lee, Changick Kim

In this paper, we propose SAFENet that is designed to leverage semantic information to overcome the limitations of the photometric loss.

Depth Prediction Monocular Depth Estimation +1

Arbitrary Style Transfer using Graph Instance Normalization

no code implementations6 Oct 2020 Dongki Jung, Seunghan Yang, Jaehoon Choi, Changick Kim

Style transfer is the image synthesis task, which applies a style of one image to another while preserving the content.

Domain Adaptation Image-to-Image Translation +2

Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection

no code implementations ICCV 2019 Seunghyeon Kim, Jaehoon Choi, Taekyung Kim, Changick Kim

Experimental results show that our approach effectively improves the performance of the one-stage object detection in unsupervised domain adaptation setting.

object-detection Object Detection +1

Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation

no code implementations1 Aug 2019 Jaehoon Choi, Minki Jeong, Taekyung Kim, Changick Kim

To learn target discriminative representations, using pseudo-labels is a simple yet effective approach for unsupervised domain adaptation.

Semi-Supervised Image Classification Unsupervised Domain Adaptation

Learning User Preferences and Understanding Calendar Contexts for Event Scheduling

1 code implementation5 Sep 2018 Donghyeon Kim, Jinhyuk Lee, Donghee Choi, Jaehoon Choi, Jaewoo Kang

With online calendar services gaining popularity worldwide, calendar data has become one of the richest context sources for understanding human behavior.


Cannot find the paper you are looking for? You can Submit a new open access paper.