.png){kind=icon}
.png){kind=icon}
Return
Depth Map Refinement for Immersive Video
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3050554.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this article, we propose a depth map refinement method that increases the quality of immersive video. The proposal highly enhances the inter-view consistency of depth maps (estimated or acquired by any method), crucial for achieving the required fidelity of the virtual view synthesis process. In the described method, only information from depth maps is used, as the use of texture can introduce errors in the refinement, mostly due to inter-view color inconsistencies and noise. In order to evaluate the performance of the proposal and compare it with the state of the art, three experiments were conducted. To test the influence of the refinement on the encoding of immersive video, four sets of depth maps (original, refined with the synthesis-based refinement, a bilateral filter, and with the proposal) were encoded with the MPEG Immersive Video (MIV) encoder. In the second experiment, in order to provide a direct evaluation of the accuracy of depth maps, the Middlebury database comparison was performed. In the third experiment, the temporal consistency of depth maps was assessed by measuring the efficiency of encoding of the virtual views. The experiments showed both a high increase of the virtual view synthesis quality in immersive video applications and higher similarity to ground-truth after the refinement of estimated depth maps. The usefulness of the proposal was appreciated and confirmed by the experts of the ISO/IEC MPEG group for immersive video and the method became the MPEG Reference Software for the depth refinement. The implementation of the method is publicly available for other researchers.
Keywords:
Estimation
Color
Proposals
Encoding
Transform coding
Cameras
Redundancy
Depth map refinement
immersive video
virtual navigation
multiview stereo
inter-view consistency
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.