Depth map artefacts reduction: a review

Ibrahim, Mostafa Mahmoud; Liu, Qiong; Khan, Rizwan; Yang, Jingyu; Adeli, Ehsan; You, Yang

doi:10.1049/iet-ipr.2019.1622

Cited by 15 publications

(5 citation statements)

References 150 publications

(216 reference statements)

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“…Hence, the proposed vari-focal light field camera enables the acquiring of depth information from the near to far area compared to conventional light field camera systems. Further advancement in disparity map extraction can be achieved by additional post-processing to reduce the disparity map artefacts caused by excessive light reflection in the scene [28,29].…”

Section: Resultsmentioning

confidence: 99%

Vari-Focal Light Field Camera for Extended Depth of Field

Kim

Chang

et al. 2021

Micromachines

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The light field camera provides a robust way to capture both spatial and angular information within a single shot. One of its important applications is in 3D depth sensing, which can extract depth information from the acquired scene. However, conventional light field cameras suffer from shallow depth of field (DoF). Here, a vari-focal light field camera (VF-LFC) with an extended DoF is newly proposed for mid-range 3D depth sensing applications. As a main lens of the system, a vari-focal lens with four different focal lengths is adopted to extend the DoF up to ~15 m. The focal length of the micro-lens array (MLA) is optimized by considering the DoF both in the image plane and in the object plane for each focal length. By dividing measurement regions with each focal length, depth estimation with high reliability is available within the entire DoF. The proposed VF-LFC is evaluated by the disparity data extracted from images with different distances. Moreover, the depth measurement in an outdoor environment demonstrates that our VF-LFC could be applied in various fields such as delivery robots, autonomous vehicles, and remote sensing drones.

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Section: Resultsmentioning

confidence: 99%

Vari-Focal Light Field Camera for Extended Depth of Field

Kim

Chang

et al. 2021

Micromachines

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“…First of all, a depth estimator must deal with decoded input views, which may be severely distorted by the compression. Thus, besides the challenges typically faced by the imagebased depth estimation, like texture-less regions, occlusions, or specular reflections [67], the compression-induced errors can affect the robustness of inter-view matching. During the video compression, a large part of high-frequency components can be notched out, which in turn leads to blurring or, in the worst case, an edge shift in the image.…”

Section: A Requirements For a Decoder-side Depth Estimatormentioning

confidence: 99%

Overview and Efficiency of Decoder-Side Depth Estimation in MPEG Immersive Video

Mieloch¹,

Garus

Milovanović

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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This paper presents the overview and rationale behind the Decoder-Side Depth Estimation (DSDE) mode of the MPEG Immersive Video (MIV) standard, using the Geometry Absent profile, for efficient compression of immersive multiview video. A MIV bitstream generated by an encoder operating in the DSDE mode does not include depth maps. It only contains the information required to reconstruct them in the client or in the cloud: decoded views and metadata. The paper explains the technical details and techniques supported by this novel MIV DSDE mode. The description additionally includes the specification on Geometry Assistance Supplemental Enhancement Information which helps to reduce the complexity of depth estimation, when performed in the cloud or at the decoder side. The depth estimation in MIV is a non-normative part of the decoding process, therefore, any method can be used to compute the depth maps. This paper lists a set of requirements for depth estimation, induced by the specific characteristics of the DSDE. The depth estimation reference software, continuously and collaboratively developed with MIV to meet these requirements, is presented in this paper. Several original experimental results are presented. The efficiency of the DSDE is compared to two MIV profiles. The combined non-transmission of depth maps and efficient coding of textures enabled by the DSDE leads to efficient compression and rendering quality improvement compared to the usual encoder-side depth estimation. Moreover, results of the first evaluation of state-of-the-art multiview depth estimators in the DSDE context, including machine learning techniques, are presented.

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“…However, the use of low-cost depth cameras also has limitations, such as variable lighting conditions and background clutter, which can negatively affect the quality of the tracking [3]. Moreover, depth maps recorded by depth camera sensors are frequently distorted with various sorts of spatial and temporal artifacts such as Gaussian noise and hole pixels as spatial artifacts, as well as flickering between depth video frames [4]. By separating poses and removing context, the misleading context can be avoided.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images

Ryselis

Blažauskas

Damaševičius

et al. 2022

Sensors

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The identification of human activities from videos is important for many applications. For such a task, three-dimensional (3D) depth images or image sequences (videos) can be used, which represent the positioning information of the objects in a 3D scene obtained from depth sensors. This paper presents a framework to create foreground–background masks from depth images for human body segmentation. The framework can be used to speed up the manual depth image annotation process with no semantics known beforehand and can apply segmentation using a performant algorithm while the user only adjusts the parameters, or corrects the automatic segmentation results, or gives it hints by drawing a boundary of the desired object. The approach has been tested using two different datasets with a human in a real-world closed environment. The solution has provided promising results in terms of reducing the manual segmentation time from the perspective of the processing time as well as the human input time.

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Depth map artefacts reduction: a review

Cited by 15 publications

References 150 publications

Vari-Focal Light Field Camera for Extended Depth of Field

Vari-Focal Light Field Camera for Extended Depth of Field

Overview and Efficiency of Decoder-Side Depth Estimation in MPEG Immersive Video

Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images

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