Active Stereo Matching Benchmark for 3D Reconstruction using Multi-view Depths

Mingyu Jang; Seongmin Lee; Jiwoo Kang; Sanghoon Lee

doi:10.1109/ICSIPA52582.2021.9576787

Active Stereo Matching Benchmark for 3D Reconstruction using Multi-view Depths

Mingyu Jang, Seongmin Lee, Jiwoo Kang, Sanghoon Lee

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

With the advance of 3D entertainment, 3D reconstruction has been widely researched. Recently, for the 3D reconstruction, multi-view depth images are generally used due to the wide availability of commercial RGBD sensors. The depth image can be directly acquired from the specific sensor or estimated from the stereo images by using a stereo matching algorithm. The performance of the depth estimation using a specific sensor is only dependent on the sensor performance. However, since the stereo matching method is dependent on stereo matching accuracy, a more accurate depth can be obtained from the high accuracy stereo matching method. Therefore, we focus on the stereo matching method for estimating the depth image. In this paper, we present the benchmark on the active stereo matching method for 3D reconstruction. Through the quantitative and qualitative benchmarks, we analyze and visualize the depth estimation and 3D reconstruction results. By presenting the active stereo matching benchmark, we provide guidance for 3D reconstruction using multi-view depths.

Original language	English
Title of host publication	Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665435925
DOIs	https://doi.org/10.1109/ICSIPA52582.2021.9576787
Publication status	Published - 2021
Event	7th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021 - Virtual, Online, Malaysia Duration: 2021 Sept 13 → 2021 Sept 15

Publication series

Name	Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021

Conference

Conference	7th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021
Country/Territory	Malaysia
City	Virtual, Online
Period	21/9/13 → 21/9/15

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea Government (Ministry of Science and ICT, MSIT) under Grant NRF-2020R1A2C3011697.

Publisher Copyright:
© 2021 IEEE

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Signal Processing
Health Informatics

Access to Document

10.1109/ICSIPA52582.2021.9576787

Cite this

Jang, M., Lee, S., Kang, J., & Lee, S. (2021). Active Stereo Matching Benchmark for 3D Reconstruction using Multi-view Depths. In Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021 (Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSIPA52582.2021.9576787

Jang, Mingyu ; Lee, Seongmin ; Kang, Jiwoo et al. / Active Stereo Matching Benchmark for 3D Reconstruction using Multi-view Depths. Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021).

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title = "Active Stereo Matching Benchmark for 3D Reconstruction using Multi-view Depths",

abstract = "With the advance of 3D entertainment, 3D reconstruction has been widely researched. Recently, for the 3D reconstruction, multi-view depth images are generally used due to the wide availability of commercial RGBD sensors. The depth image can be directly acquired from the specific sensor or estimated from the stereo images by using a stereo matching algorithm. The performance of the depth estimation using a specific sensor is only dependent on the sensor performance. However, since the stereo matching method is dependent on stereo matching accuracy, a more accurate depth can be obtained from the high accuracy stereo matching method. Therefore, we focus on the stereo matching method for estimating the depth image. In this paper, we present the benchmark on the active stereo matching method for 3D reconstruction. Through the quantitative and qualitative benchmarks, we analyze and visualize the depth estimation and 3D reconstruction results. By presenting the active stereo matching benchmark, we provide guidance for 3D reconstruction using multi-view depths.",

author = "Mingyu Jang and Seongmin Lee and Jiwoo Kang and Sanghoon Lee",

note = "Funding Information: This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea Government (Ministry of Science and ICT, MSIT) under Grant NRF-2020R1A2C3011697. Publisher Copyright: {\textcopyright} 2021 IEEE; 7th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021 ; Conference date: 13-09-2021 Through 15-09-2021",

year = "2021",

doi = "10.1109/ICSIPA52582.2021.9576787",

language = "English",

series = "Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021",

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booktitle = "Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021",

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Jang, M, Lee, S, Kang, J & Lee, S 2021, Active Stereo Matching Benchmark for 3D Reconstruction using Multi-view Depths. in Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021. Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021, Institute of Electrical and Electronics Engineers Inc., 7th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021, Virtual, Online, Malaysia, 21/9/13. https://doi.org/10.1109/ICSIPA52582.2021.9576787

Active Stereo Matching Benchmark for 3D Reconstruction using Multi-view Depths. / Jang, Mingyu; Lee, Seongmin; Kang, Jiwoo et al.

Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Jang, Mingyu

AU - Lee, Seongmin

AU - Kang, Jiwoo

AU - Lee, Sanghoon

N1 - Funding Information: This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea Government (Ministry of Science and ICT, MSIT) under Grant NRF-2020R1A2C3011697. Publisher Copyright: © 2021 IEEE

PY - 2021

Y1 - 2021

N2 - With the advance of 3D entertainment, 3D reconstruction has been widely researched. Recently, for the 3D reconstruction, multi-view depth images are generally used due to the wide availability of commercial RGBD sensors. The depth image can be directly acquired from the specific sensor or estimated from the stereo images by using a stereo matching algorithm. The performance of the depth estimation using a specific sensor is only dependent on the sensor performance. However, since the stereo matching method is dependent on stereo matching accuracy, a more accurate depth can be obtained from the high accuracy stereo matching method. Therefore, we focus on the stereo matching method for estimating the depth image. In this paper, we present the benchmark on the active stereo matching method for 3D reconstruction. Through the quantitative and qualitative benchmarks, we analyze and visualize the depth estimation and 3D reconstruction results. By presenting the active stereo matching benchmark, we provide guidance for 3D reconstruction using multi-view depths.

AB - With the advance of 3D entertainment, 3D reconstruction has been widely researched. Recently, for the 3D reconstruction, multi-view depth images are generally used due to the wide availability of commercial RGBD sensors. The depth image can be directly acquired from the specific sensor or estimated from the stereo images by using a stereo matching algorithm. The performance of the depth estimation using a specific sensor is only dependent on the sensor performance. However, since the stereo matching method is dependent on stereo matching accuracy, a more accurate depth can be obtained from the high accuracy stereo matching method. Therefore, we focus on the stereo matching method for estimating the depth image. In this paper, we present the benchmark on the active stereo matching method for 3D reconstruction. Through the quantitative and qualitative benchmarks, we analyze and visualize the depth estimation and 3D reconstruction results. By presenting the active stereo matching benchmark, we provide guidance for 3D reconstruction using multi-view depths.

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M3 - Conference contribution

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BT - Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 13 September 2021 through 15 September 2021

ER -

Jang M, Lee S, Kang J, Lee S. Active Stereo Matching Benchmark for 3D Reconstruction using Multi-view Depths. In Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings of the 2021 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021). doi: 10.1109/ICSIPA52582.2021.9576787

Active Stereo Matching Benchmark for 3D Reconstruction using Multi-view Depths

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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