Depth Map Compression Using Color-Driven Isotropic Segmentation and Regularised Reconstruction

Georgiev, Mihail; Belyaev, Evgeny; Gotchev, Atanas

doi:10.1109/dcc.2015.88

Cited by 7 publications

(9 citation statements)

References 19 publications

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“…Previously, we have proposed techniques for depth resampling and 3D fusion for the case of an asymmetric non-confocal V+Z camera setup, where the depth is sensed in low-sensing conditions [43], [44], as well as techniques for near-lossless depth encoding [45], [46]. In the present work, we present a general framework, which addresses both cases.…”

Section: Relation With Previous Workmentioning

confidence: 99%

“…We further extend the technical stages of super-pixel (SP) segmentation, resampling, regularization, encoding, and 3D fusion. More specifically, we modify the segmentation clustering stage proposed in [45], [44] to ensure border congruency at hierarchical refinement levels and seed the SP clusters for non-uniform data samples to serve the case of projected data. Furthermore, we address the problem of possible misalignment between V and Z modalities caused by sensing artifacts.…”

Section: Relation With Previous Workmentioning

confidence: 99%

“…Second, using the mass center locations for seeding points, prevents the occurrence of a misaligned clustering done on finer mosaic scales for the consecutive iterations. The congruency of SP boundaries is of vital importance for simplifying the encoding approach and improving the speed and quality performance of the originally proposed compression methods [44], [45].…”

Section: B Multi-layer Congruent Super-pixel Clusteringmentioning

confidence: 99%

“…The segmentation structure comes from the color modality and can be reproduced, thus it does not need to be encoded. The map itself is encoded in a predictive sequence P Z similarly to ''JPEG-LS'' standard [21] and described in detail in our previous work [45].…”

Section: ) Depth Refinement By Super-pixel Partitioningmentioning

confidence: 99%

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A General Framework for Depth Compression and Multi-Sensor Fusion in Asymmetric View-Plus-Depth 3D Representation

2020

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We present a general framework which can handle different processing stages of the three-dimensional (3D) scene representation referred to as ''view-plus-depth'' (V+Z). The main component of the framework is the relation between the depth map and the super-pixel segmentation of the color image. We propose a hierarchical super-pixel segmentation which keeps the same boundaries between hierarchical segmentation layers. Such segmentation allows for a corresponding depth segmentation, decimation and reconstruction with varying quality and is instrumental in tasks such as depth compression and 3D data fusion. For the latter we utilize a cross-modality reconstruction filter which is adaptive to the size of the refining super-pixel segments. We propose a novel depth encoding scheme, which includes specific arithmetic encoder and handles misalignment outliers. We demonstrate that our scheme is especially applicable for low bit-rate depth encoding and for fusing color and depth data, where the latter is noisy and with lower spatial resolution.

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Section: Relation With Previous Workmentioning

confidence: 99%

Section: Relation With Previous Workmentioning

confidence: 99%

Section: B Multi-layer Congruent Super-pixel Clusteringmentioning

confidence: 99%

Section: ) Depth Refinement By Super-pixel Partitioningmentioning

confidence: 99%

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A General Framework for Depth Compression and Multi-Sensor Fusion in Asymmetric View-Plus-Depth 3D Representation

2020

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“…Color data can also been used to assist the compression of depth information as in [14], [15], [16] and [17]. The coding of depth videos has also been considered, e.g., in [18] the idea that pixels with similar depth have similar motion is exploited.…”

Section: Related Workmentioning

confidence: 99%

Depth map coding with elastic contours and 3D surface prediction

Calemme¹,

Zanuttigh²,

Milani³

et al. 2016

2016 IEEE International Conference on Image Processing (ICIP)

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Improved Depth Compression by Depth Downsampling Guided by Color Super-Pixel Refinement Segmentation

Georgiev¹,

Gotchev

2018

2018 Data Compression Conference

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Depth Map Compression Using Color-Driven Isotropic Segmentation and Regularised Reconstruction

Cited by 7 publications

References 19 publications

A General Framework for Depth Compression and Multi-Sensor Fusion in Asymmetric View-Plus-Depth 3D Representation

A General Framework for Depth Compression and Multi-Sensor Fusion in Asymmetric View-Plus-Depth 3D Representation

Depth map coding with elastic contours and 3D surface prediction

Improved Depth Compression by Depth Downsampling Guided by Color Super-Pixel Refinement Segmentation

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