no code implementations • CVPR 2023 • Jaehoon Choi, Dongki Jung, Taejae Lee, SangWook Kim, Youngdong Jung, Dinesh Manocha, Donghwan Lee
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.
no code implementations • 10 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.
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.
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.
no code implementations • 10 Nov 2020 • Jaehoon Choi, Dongki Jung, Yonghan Lee, Deokhwa Kim, Dinesh Manocha, Donghwan Lee
We present a novel algorithm for self-supervised monocular depth completion.
1 code implementation • 6 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.
no code implementations • 6 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.
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.
no code implementations • ICCV 2019 • Jaehoon Choi, Taekyung Kim, Changick Kim
Unsupervised domain adaptation seeks to adapt the model trained on the source domain to the target domain.
no code implementations • 1 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
1 code implementation • 5 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.
1 code implementation • 17 Oct 2017 • Li Yi, Lin Shao, Manolis Savva, Haibin Huang, Yang Zhou, Qirui Wang, Benjamin Graham, Martin Engelcke, Roman Klokov, Victor Lempitsky, Yuan Gan, Pengyu Wang, Kun Liu, Fenggen Yu, Panpan Shui, Bingyang Hu, Yan Zhang, Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Minki Jeong, Jaehoon Choi, Changick Kim, Angom Geetchandra, Narasimha Murthy, Bhargava Ramu, Bharadwaj Manda, M. Ramanathan, Gautam Kumar, P Preetham, Siddharth Srivastava, Swati Bhugra, Brejesh lall, Christian Haene, Shubham Tulsiani, Jitendra Malik, Jared Lafer, Ramsey Jones, Siyuan Li, Jie Lu, Shi Jin, Jingyi Yu, Qi-Xing Huang, Evangelos Kalogerakis, Silvio Savarese, Pat Hanrahan, Thomas Funkhouser, Hao Su, Leonidas Guibas
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.