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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

Flow chart for MV outlier detection and processing in our proposed scheme.
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pone-0055586-g003: Flow chart for MV outlier detection and processing in our proposed scheme.

Mentions: To this end, we propose a scheme shown in Figure 3 for the MV processing. In this scheme, all MVs of blocks are processed by 3 steps to eliminate the outliers. In the first step, a foreground and background detector is applied on the block to locate which part this block belongs to. The object boundary is crucial in distinguishing foreground and background, and thus a texture extractor can be applied to generate a texture mask map on this color image. Let be the coordinate of current block in , where are the 8 adjacent neighboring blocks around , is the corresponding MV for , and is the corresponding block for and in respectively, where . There are three cases for foreground and background detection:


Remote dynamic three-dimensional scene reconstruction.

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

Flow chart for MV outlier detection and processing in our proposed scheme.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3646941&req=5

pone-0055586-g003: Flow chart for MV outlier detection and processing in our proposed scheme.
Mentions: To this end, we propose a scheme shown in Figure 3 for the MV processing. In this scheme, all MVs of blocks are processed by 3 steps to eliminate the outliers. In the first step, a foreground and background detector is applied on the block to locate which part this block belongs to. The object boundary is crucial in distinguishing foreground and background, and thus a texture extractor can be applied to generate a texture mask map on this color image. Let be the coordinate of current block in , where are the 8 adjacent neighboring blocks around , is the corresponding MV for , and is the corresponding block for and in respectively, where . There are three cases for foreground and background detection:

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