Plane fitting and depth variance based upsampling for noisy depth map from 3D-ToF cameras in real-time

Kazuki Matsumoto, Francois De Sorbier, Hideo Saito

研究成果: Conference contribution

抄録

Recent advances of ToF depth sensor devices enables us to easily retrieve scene depth data with high frame rates. However, the resolution of the depth map captured from these devices is much lower than that of color images and the depth data suffers from the optical noise effects. In this paper, we propose an efficient algorithm that upsamples depth map captured by ToF depth cameras and reduces noise. The upsampling is carried out by applying plane based interpolation to the groups of points similar to planar structures and depth variance based joint bilateral upsampling to curved or bumpy surface points. For dividing the depth map into piecewise planar areas, we apply superpixel segmentation and graph component labeling. In order to distinguish planar areas and curved areas, we evaluate the reliability of detected plane structures. Compared with other state-of-the-art algorithms, our method is observed to produce an upsampled depth map that is smoothed and closer to the ground truth depth map both visually and numerically. Since the algorithm is parallelizable, it can work in real-time by utilizing highly parallel processing capabilities of modern commodity GPUs.

元の言語English
ホスト出版物のタイトルICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings
出版者SciTePress
ページ150-157
ページ数8
2
ISBN(印刷物)9789897580772
出版物ステータスPublished - 2015
イベント4th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015 - Lisbon, Portugal
継続期間: 2015 1 102015 1 12

Other

Other4th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015
Portugal
Lisbon
期間15/1/1015/1/12

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Cameras
Labeling
Interpolation
Color
Sensors
Processing

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

これを引用

Matsumoto, K., De Sorbier, F., & Saito, H. (2015). Plane fitting and depth variance based upsampling for noisy depth map from 3D-ToF cameras in real-time. : ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings (巻 2, pp. 150-157). SciTePress.

Plane fitting and depth variance based upsampling for noisy depth map from 3D-ToF cameras in real-time. / Matsumoto, Kazuki; De Sorbier, Francois; Saito, Hideo.

ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings. 巻 2 SciTePress, 2015. p. 150-157.

研究成果: Conference contribution

Matsumoto, K, De Sorbier, F & Saito, H 2015, Plane fitting and depth variance based upsampling for noisy depth map from 3D-ToF cameras in real-time. : ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings. 巻. 2, SciTePress, pp. 150-157, 4th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015, Lisbon, Portugal, 15/1/10.
Matsumoto K, De Sorbier F, Saito H. Plane fitting and depth variance based upsampling for noisy depth map from 3D-ToF cameras in real-time. : ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings. 巻 2. SciTePress. 2015. p. 150-157
Matsumoto, Kazuki ; De Sorbier, Francois ; Saito, Hideo. / Plane fitting and depth variance based upsampling for noisy depth map from 3D-ToF cameras in real-time. ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings. 巻 2 SciTePress, 2015. pp. 150-157
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AB - Recent advances of ToF depth sensor devices enables us to easily retrieve scene depth data with high frame rates. However, the resolution of the depth map captured from these devices is much lower than that of color images and the depth data suffers from the optical noise effects. In this paper, we propose an efficient algorithm that upsamples depth map captured by ToF depth cameras and reduces noise. The upsampling is carried out by applying plane based interpolation to the groups of points similar to planar structures and depth variance based joint bilateral upsampling to curved or bumpy surface points. For dividing the depth map into piecewise planar areas, we apply superpixel segmentation and graph component labeling. In order to distinguish planar areas and curved areas, we evaluate the reliability of detected plane structures. Compared with other state-of-the-art algorithms, our method is observed to produce an upsampled depth map that is smoothed and closer to the ground truth depth map both visually and numerically. Since the algorithm is parallelizable, it can work in real-time by utilizing highly parallel processing capabilities of modern commodity GPUs.

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