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Remote dynamic three-dimensional scene reconstruction.

Yang Y, Liu Q, Ji R, Gao Y - PLoS ONE (2013)

Bottom Line: However, in most of the remote transmission systems, only the compressed color video stream is available.Our method rectifies the inaccurate motion vectors by analyzing and compensating their quality losses, motion vector absence in spatial prediction, and dislocation in near-boundary region.This rectification ensures the depth maps can be compensated in both video-rate and high resolution at the terminal side towards reducing the system consumption on both the compression and transmission.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.

ABSTRACT
Remote dynamic three-dimensional (3D) scene reconstruction renders the motion structure of a 3D scene remotely by means of both the color video and the corresponding depth maps. It has shown a great potential for telepresence applications like remote monitoring and remote medical imaging. Under this circumstance, video-rate and high resolution are two crucial characteristics for building a good depth map, which however mutually contradict during the depth sensor capturing. Therefore, recent works prefer to only transmit the high-resolution color video to the terminal side, and subsequently the scene depth is reconstructed by estimating the motion vectors from the video, typically using the propagation based methods towards a video-rate depth reconstruction. However, in most of the remote transmission systems, only the compressed color video stream is available. As a result, color video restored from the streams has quality losses, and thus the extracted motion vectors are inaccurate for depth reconstruction. In this paper, we propose a precise and robust scheme for dynamic 3D scene reconstruction by using the compressed color video stream and their inaccurate motion vectors. Our method rectifies the inaccurate motion vectors by analyzing and compensating their quality losses, motion vector absence in spatial prediction, and dislocation in near-boundary region. This rectification ensures the depth maps can be compensated in both video-rate and high resolution at the terminal side towards reducing the system consumption on both the compression and transmission. Our experiments validate that the proposed scheme is robust for depth map and dynamic scene reconstruction on long propagation distance, even with high compression ratio, outperforming the benchmark approaches with at least 3.3950 dB quality gains for remote applications.

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Related in: MedlinePlus

Parameter analysis for thresholds  and  on the Kendo and Lovebird test materials in the case of using different QP parameters.(A) Bad Point Ratio curves for different  and QP parameter when  is fixed as 8. Experiments are performed on Kendo test sequence. (B) Bad Point Ratio curves for different  and QP parameter when  is fixed as 8. Experiments are performed on Lovebird test sequence. (C) Bad Point Ratio curves for different  and QP parameters when  is fixed as 1. Experiments are performed on Kendo test sequence. (D) Bad Point Ratio curves for different  and QP parameters when  is fixed as 1. Experiments are performed on Lovebird test sequence.
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pone-0055586-g007: Parameter analysis for thresholds and on the Kendo and Lovebird test materials in the case of using different QP parameters.(A) Bad Point Ratio curves for different and QP parameter when is fixed as 8. Experiments are performed on Kendo test sequence. (B) Bad Point Ratio curves for different and QP parameter when is fixed as 8. Experiments are performed on Lovebird test sequence. (C) Bad Point Ratio curves for different and QP parameters when is fixed as 1. Experiments are performed on Kendo test sequence. (D) Bad Point Ratio curves for different and QP parameters when is fixed as 1. Experiments are performed on Lovebird test sequence.

Mentions: Figure 7 demonstrates the analysis on and when different test materials (Kendo and Lovebird) and QP settings (22, 32 and 42) are involved. Bad Point Ratio is employed to measure the quality of reconstructed depth map, and a pixel is defined as bad point if the the corresponding depth value difference between reconstructed and benchmark depth maps is bigger than 1. The Bad Point Ratio in Figure 7 is an average value that is calculated by 16 sequentially reconstructed depth maps. Figures 7 (A) and (B) show that the variation curves of when is fixed as 8. These curves indicate that for both the test materials and all QP settings, the Bad Point Ratio becomes bigger and tends to be stable when becomes bigger. Practically, smaller Bad Point Ratio is preferred for depth map reconstruction. However, the curves are not stable when is small, and thus smaller may be unsuitable for most test materials. As shown by Figures 7 (A) and (B), the performance of all curves tends to be stable when , and the Bad Point Ratio is small enough for practical usage. Therefore, we set in the rest of our experiments. After that, is analyzed based on the above discussion of , and the results are given by Figures 7 (C) and (D). These curves also indicate that for both test materials and all QP settings, the Bad Point Ratio becomes bigger when is become bigger. All curves have similar trend that smaller Bad Point Ratio can be obtained by smaller . Therefore, the smallest is selected.


Remote dynamic three-dimensional scene reconstruction.

Yang Y, Liu Q, Ji R, Gao Y - PLoS ONE (2013)

Parameter analysis for thresholds  and  on the Kendo and Lovebird test materials in the case of using different QP parameters.(A) Bad Point Ratio curves for different  and QP parameter when  is fixed as 8. Experiments are performed on Kendo test sequence. (B) Bad Point Ratio curves for different  and QP parameter when  is fixed as 8. Experiments are performed on Lovebird test sequence. (C) Bad Point Ratio curves for different  and QP parameters when  is fixed as 1. Experiments are performed on Kendo test sequence. (D) Bad Point Ratio curves for different  and QP parameters when  is fixed as 1. Experiments are performed on Lovebird test sequence.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3646941&req=5

pone-0055586-g007: Parameter analysis for thresholds and on the Kendo and Lovebird test materials in the case of using different QP parameters.(A) Bad Point Ratio curves for different and QP parameter when is fixed as 8. Experiments are performed on Kendo test sequence. (B) Bad Point Ratio curves for different and QP parameter when is fixed as 8. Experiments are performed on Lovebird test sequence. (C) Bad Point Ratio curves for different and QP parameters when is fixed as 1. Experiments are performed on Kendo test sequence. (D) Bad Point Ratio curves for different and QP parameters when is fixed as 1. Experiments are performed on Lovebird test sequence.
Mentions: Figure 7 demonstrates the analysis on and when different test materials (Kendo and Lovebird) and QP settings (22, 32 and 42) are involved. Bad Point Ratio is employed to measure the quality of reconstructed depth map, and a pixel is defined as bad point if the the corresponding depth value difference between reconstructed and benchmark depth maps is bigger than 1. The Bad Point Ratio in Figure 7 is an average value that is calculated by 16 sequentially reconstructed depth maps. Figures 7 (A) and (B) show that the variation curves of when is fixed as 8. These curves indicate that for both the test materials and all QP settings, the Bad Point Ratio becomes bigger and tends to be stable when becomes bigger. Practically, smaller Bad Point Ratio is preferred for depth map reconstruction. However, the curves are not stable when is small, and thus smaller may be unsuitable for most test materials. As shown by Figures 7 (A) and (B), the performance of all curves tends to be stable when , and the Bad Point Ratio is small enough for practical usage. Therefore, we set in the rest of our experiments. After that, is analyzed based on the above discussion of , and the results are given by Figures 7 (C) and (D). These curves also indicate that for both test materials and all QP settings, the Bad Point Ratio becomes bigger when is become bigger. All curves have similar trend that smaller Bad Point Ratio can be obtained by smaller . Therefore, the smallest is selected.

Bottom Line: However, in most of the remote transmission systems, only the compressed color video stream is available.Our method rectifies the inaccurate motion vectors by analyzing and compensating their quality losses, motion vector absence in spatial prediction, and dislocation in near-boundary region.This rectification ensures the depth maps can be compensated in both video-rate and high resolution at the terminal side towards reducing the system consumption on both the compression and transmission.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.

ABSTRACT
Remote dynamic three-dimensional (3D) scene reconstruction renders the motion structure of a 3D scene remotely by means of both the color video and the corresponding depth maps. It has shown a great potential for telepresence applications like remote monitoring and remote medical imaging. Under this circumstance, video-rate and high resolution are two crucial characteristics for building a good depth map, which however mutually contradict during the depth sensor capturing. Therefore, recent works prefer to only transmit the high-resolution color video to the terminal side, and subsequently the scene depth is reconstructed by estimating the motion vectors from the video, typically using the propagation based methods towards a video-rate depth reconstruction. However, in most of the remote transmission systems, only the compressed color video stream is available. As a result, color video restored from the streams has quality losses, and thus the extracted motion vectors are inaccurate for depth reconstruction. In this paper, we propose a precise and robust scheme for dynamic 3D scene reconstruction by using the compressed color video stream and their inaccurate motion vectors. Our method rectifies the inaccurate motion vectors by analyzing and compensating their quality losses, motion vector absence in spatial prediction, and dislocation in near-boundary region. This rectification ensures the depth maps can be compensated in both video-rate and high resolution at the terminal side towards reducing the system consumption on both the compression and transmission. Our experiments validate that the proposed scheme is robust for depth map and dynamic scene reconstruction on long propagation distance, even with high compression ratio, outperforming the benchmark approaches with at least 3.3950 dB quality gains for remote applications.

Show MeSH
Related in: MedlinePlus