Robotics and Computer Vision Lab

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International Journal
2017.10.19 16:30

Geometry Guided 3D Propagation for Depth from Small Motion

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저 자 Seunghak Shin, Sunghoon Im, Inwook Shim, Hae-Gon Jeon, In So Kweon
학 회 IEEE Signal Processing Letters
Notes This work was supported by the “Development of core technology for advanced locomotion/manipulation based on high-speed/power robot platform and robot intelligence,” project from the Korea Evaluation Institute of Industrial Technology of the Republic of Korea. The work of S. Im and H.-G. Jeon was supported in part by Global Ph.D. Fellowship Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant NRF-2016907531 and Grant NRF-2015034617.
논문일시(Year) 2017
논문일시(Month) 12
http://ieeexplore.ieee.org/document/8063412/In this letter, we present an accurate Depth from Small Motion approach, which reconstructs three-dimensional (3-D) depth from image sequences with extremely narrow base-lines.We start with estimating sparse 3-D points and camera poses via the structure from motion method. For dense depth reconstruction, we propose a novel depth propagation using a geometric guidance term that considers not only the geometric constraint from the surface normal, but also color consistency. In addition, we propose an accurate surface normal estimation method with a multiple range search so that the normal vector can guide the direction of the depth propagation precisely. The major benefit of our depth propagation method is that it obtains detailed structures of a scene without fronto-parallel bias.We validate our method using various indoor and outdoor datasets, and both qualitative and quantitative experimental results show that our new algorithm consistently generates better 3-D depth information than the results of existing state-of-the-art methods.

List of Articles
120. A Simple and Light-weight Attention Module for Convolutional Neural Networks
Jongchan Park*, Sanghyun Woo*, Joon-Young Lee, In-So Kweon
International Journal of Computer Vision (IJCV) 2019 / 12
119. Recurrent Temporal Aggregation Framework for Deep Video Inpainting
Dahun Kim*, Sanghyun Woo*, Joon-Young Lee, In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2019 / 11
118. Learning to Localize Sound Sources in Visual Scenes: Analysis and Applications
Arda Senocak, Tae-Hyun Oh, Junsik Kim, Ming-Hsuan Yang, In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2019 / 10
117. Ring Difference Filter for Fast and Noise Robust Depth from Focus
Hae-Gon Jeon, Jaeheung Surh, Sunghoon Im, In So Kweon
IEEE Transactions Image Processing (TIP) 2019 / 8
116. Deep Iterative Frame Interpolation for Full-frame Video Stabilization
Jinsoo Choi, In So Kweon
ACM Transactions on Graphics (TOG) / SIGGRAPH Asia 2019 / 11
115. Globally Optimal Inlier Set Maximization for Atlanta World Understanding
Kyungdon Joo, Tae-Hyun Oh, In So Kweon, Jean-Charles Bazin
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2019 / 3
114. One-Day Outdoor Photometric Stereo Using Skylight Estimation
Jiyoung Jung, Joon-Young Lee, In So Kweon
International Journal of Computer Vision (IJCV) 2019 / 8
113. High-Fidelity Depth Upsampling Using the Self-Learning Framework
Inwook Shim, Tae-Hyun Oh, In So Kweon
Sensors 2019 / 01
112. Robust Depth Estimation using Auto-Exposure Bracketing
Sunghoon Im, Hae-Gon Jeon, In So Kweon
IEEE Transaction on Image Processing (TIP) 2018 / 12
111. Semi-calibrated Photometric Stereo
Donghyeon Cho, Yasuyuki Matsushita, Yu-Wing Tai, and In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) /
110. Deep Convolutional Neural Network for Natural Image Matting using Initial Alpha Mattes
Donghyeon Cho, Yu-Wing Tai, In So Kweon
IEEE Transactions on Image Processing (TIP), accepted /
109. Real-Time Head Pose Estimation using Multi-Task Deep Neural Network
Byungtae Ahn, Dong-Geol Choi, Jaesik Park, In So Kweon
Robotics and Autonomous Systems 2018 / 05
108. Depth from a Light Field Image with Learning-based Matching Costs
Hae-Gon Jeon, Jaesik Park, Gyeongmin Choe, Jinsun Park, Yunsu Bok, Yu-Wing Tai and In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2018 / 01.15
107. Accurate 3D Reconstruction from Small Motion Clip for Rolling Shutter Cameras
Sunghoon Im, Hyowon Ha, Gyeongmin Choe, Hae-Gon Jeon, Kyungdon Joo, In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2018 / 03
106. KAIST Multi-Spectral Day/Night Data Set for Autonomous and Assisted Driving
Yukyung Choi, Namil Kim, Soonmin Hwang, Kibaek Park, Jae Shin Yoon, Kyunghwan An and In So Kweon
Transactions on Intelligent Transportation Systems (T-ITS) 2018 / 03
105. RANUS: RGB and NIR Urban Scene Dataset for Deep Scene Parsing
Gyeongmin Choe, Seong-heum Kim, Sunghoon Im, Joon-Young Lee, Srinivasa Narasimhan, In So Kweon
IEEE Robotics and Automation Letters (RAL) 2018 / 02
104. Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution
Oleksandr Bailo , Francois Rameau , Kyungdon Joo , Jinsun Park , Oleksandr Bogdan , In So Kweon
Pattern Recognition Letters (PRL) 2018 / 02
103. On-line Initialization and Extrinsic Calibration of an Inertial Navigation System with a Relative Preintegration Method on Manifold
Dongshin Kim, Seunghak Shin, In So Kweon
IEEE Transactions on Automation Science and Engineering (TASE) 2017 / 11
102. Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time
Kyungdon Joo, Tae-Hyun Oh, Junsik Kim, In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2017 / 11
» Geometry Guided 3D Propagation for Depth from Small Motion
Seunghak Shin, Sunghoon Im, Inwook Shim, Hae-Gon Jeon, In So Kweon
IEEE Signal Processing Letters 2017 / 12
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