Junhwa Hur

Hello! I am a research scientist at Google in Cambridge, MA, USA. I received my Ph.D. at Technische Universität Darmstadt, where I was supervised by Prof. Stefan Roth Ph.D. at Visual Inference group. I received my M.Sc. at Seoul National University and B.Sc. at POSTECH.

Email / CV / Google Scholar / Github / LinkedIn

Research

I'm interested in computer vision and machine learning, especially understanding 3D dynamic scene from a monocular video through self-supervision.

	The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation Saurabh Saxena, Charles Herrmann, Junhwa Hur, Abhishek Kar, Mohammad Norouzi, Deqing Sun, and David J. Fleet NeurIPS, 2023 Oral presentation paper / arxiv / project
	A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence Junyi Zhang, Charles Herrmann, Junhwa Hur, Luisa Polania Cabrera, Varun Jampani, Deqing Sun, and Ming-Hsuan Yang NeurIPS, 2023 paper / supp / arxiv / project / code
	Self-supervised AutoFlow Hsin-Ping Huang, Charles Herrmann, Junhwa Hur, Erika Lu, Kyle Sargent, Austin Stone, Ming-Hsuan Yang, and Deqing Sun CVPR, 2023 paper / supp / arxiv / code Self-supervised AutoFlow learns to generate an optical flow training set through self-supervision on the target domain.
	RAFT-MSF: Self-Supervised Monocular Scene Flow Using Recurrent Optimizer Bayram Bayramli, Junhwa Hur, and Hongtao Lu IJCV, 2023 paper / arxiv For self-supervised monocular scene flow, our RAFT-backbone-based approach significantly improves the scene flow accuracy and even outperforms a semi-supervised method.
	Self-Supervised Surround-View Depth Estimation with Volumetric Feature Fusion Jung Hee Kim, Junhwa Hur*, Tien Phuoc Nguyen, and Seong-Gyun Jeong Equal contribution NeurIPS, 2022 paper / code Our voxel-based approach to surround-view depth estimation improves metric-scale depth accuracy and can synthesize a depth map at arbitrary rotated views.
	Joint Motion, Semantic Segmentation, Occlusion, and Depth Estimation Junhwa Hur Ph.D. Dissertation, Technische Universität Darmstadt, 2022 paper In this dissertation, we propose how to jointly formulate multiple tasks for scene understanding and what kind of benefits can be obtained from the joint estimation.
	MasKGrasp: Mask-based Grasping for Scenes with Multiple General Real-world Objects Junho Lee, Junhwa Hur, Inwoo Hwang, and Young Min Kim IROS, 2022 paper / video We introduce a mask-based grasping method that discerns multiple transparent and opaque objects and finds the optimal grasp position avoiding clutter.
	Self-Supervised Multi-Frame Monocular Scene Flow Junhwa Hur and Stefan Roth CVPR, 2021 paper / supp / arxiv / code / talk In the multi-frame setup, using ConvLSTM + warping hidden states improves the accuracy and the temporal consistency of the monocular scene flow.
	Self-Supervised Monocular Scene Flow Estimation Junhwa Hur and Stefan Roth CVPR, 2020 Oral presentation paper / supp / arxiv / code / talk We propose to estimate scene flow from only two monocular images with a CNN trained in a self-supervised manner.
	Optical Flow Estimation in the Deep Learning Age Junhwa Hur and Stefan Roth Modelling Human Motion, N. Noceti, A. Sciutti and F. Rea, Eds., Springer, 2020 paper / arxiv As a book chapter, we comprehensively review CNN-based approaches to optical flow and their technical details, including un-/semi-supervised methods.
	Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation Junhwa Hur and Stefan Roth CVPR, 2019 paper / supp / arxiv / code An iterative residual refinement scheme based on weight sharing reduces the number of network parameters and improves the accuracy of optical flow and occlusion.
	UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss Simon Meister, Junhwa Hur and Stefan Roth AAAI, 2018 Oral presentation paper / arxiv / code / slide By directly training on the target domain with an improved unsupervised loss, our method outperforms a supervised method that is pre-trained on a synthetic dataset.
	MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation Junhwa Hur and Stefan Roth ICCV, 2017 paper / supp / arxiv / code / poster The chicken-and-egg relationship between optical flow and occlusion can be nicely formulated through exploiting the symmetry properties they have.
	Joint Optical Flow and Temporally Consistent Semantic Segmentation Junhwa Hur and Stefan Roth ECCV Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving (ECCVW), 2016 Best paper award paper / arxiv / poster We propose a method for the joint estimation of optical flow and temporally consistent semantic segmentation, which closely connects the two problem domains and allows each task leverage the other.
	Generalized Deformable Spatial Pyramid: Geometry-Preserving Dense Correspondence Estimation Junhwa Hur, Hwasup Lim, Changsoo Park, and Sang Chul Ahn CVPR, 2015 paper / supp / project / video A piece-wise similarity transform along pyramid levels can approximate a non-rigid deformation in the semantic matching problem.
	3D Deformable Spatial Pyramid for Dense 3D Motion Flow of Deformable Object Junhwa Hur, Hwasup Lim, and Sang Chul Ahn International Symposium on Visual Computing (ISVC), 2014 Oral presentation paper / project
	Multi-lane Detection based on Accurate Geometric Lane Estimation in Highway Scenarios Seung-Nam Kang, Soo-Mok Lee, Junhwa Hur, and Seung-Woo Seo Intelligent Vehicles Symposium (IV), 2014 paper / video
	Multi-lane Detection in Urban Driving Environments using Conditional Random Fields Junhwa Hur, Seung-Nam Kang, and Seung-Woo Seo Intelligent Vehicles Symposium (IV), 2013 paper / project / code / video
	Multi-lane Detection in Highway and Urban Driving Environment Junhwa Hur Master’s thesis, Seoul National University, 2013 paper / project