This paper addresses the problem of compressing the colour attributes of dynamic dense point clouds. Inter-frame prediction and motion compensation are key to removing temporal redundancies and thereby reaching major ...
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ISBN:
(纸本)9798350338935
This paper addresses the problem of compressing the colour attributes of dynamic dense point clouds. Inter-frame prediction and motion compensation are key to removing temporal redundancies and thereby reaching major compression gains. For that purpose, an attribute compression scheme combining intra-frame and motion-compensated inter-frame predictions is presented. It is built upon the Test Model developed by MPEG within the Geometry-based Point Cloud Compression (G-PCC) activity to encode dense dynamic point clouds. More precisely, a motion-compensated inter-frame prediction is introduced into the RAHT encoding scheme, leveraging the local motion field already present in the geometry encoder. The best prediction mode according to rate-distortion optimization is decided at each node of the octree, encoded by means of arithmetic coding using a binary prediction tree and signalled to the decoder. Experimental results are provided using the MPEG test sequences and common test conditions. They demonstrate very significant compression gains, with average BD-Rates of -15.2%, -18.3% and -18.1% on Y, Cb and Cr colour components respectively, when compared with the current scheme with intra-frame only attribute compression.
We studied predictive coding applied to the region-adaptive hierarchical transform (RAHT) which is used for point cloud compression (PCC). RAHT is part of MPEG's geometry-based PCC test model and an intra-frame pr...
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ISBN:
(纸本)9781728163956
We studied predictive coding applied to the region-adaptive hierarchical transform (RAHT) which is used for point cloud compression (PCC). RAHT is part of MPEG's geometry-based PCC test model and an intra-frame prediction scheme for RAHT (URAHT), wherein the prediction residual is encoded rather than the voxel attributes themselves, has been shown to deliver large gains. We extend the scheme to inter-frame prediction and show that a combination of simple zero-motion-vector (ZMV) inter-frame and intra-frame predictions can provide sizeable gains over pure RAHT or over intra-frame-only prediction when compressing dynamic point clouds. An adaptive method is used such that sections where ZMV does not yield good prediction switch to intra-frame prediction, assuring the performance to be at least that of the intra-frame case. Be the gains large (in steady parts) or very small (where there is rapid motion) results show consistent positive gains coming from a simple inter- and intra-frame prediction combination.
As 3D scanning devices and depth sensors advance, dynamic point clouds have attracted increasing attention as a format for 3D objects in motion, with applications in various fields such as immersive telepresence, navi...
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As 3D scanning devices and depth sensors advance, dynamic point clouds have attracted increasing attention as a format for 3D objects in motion, with applications in various fields such as immersive telepresence, navigation for autonomous driving and gaming. Nevertheless, the tremendous amount of data in dynamic point clouds significantly burden transmission and storage. To this end, we propose a complete compression framework for attributes of 3D dynamic point clouds, focusing on optimal inter-coding. Firstly, we derive the optimal inter-prediction and predictive transform coding assuming the Gaussian Markov Random Field model with respect to a spatio-temporal graph underlying the attributes of dynamic point clouds. The optimal predictive transform proves to be the Generalized Graph Fourier Transform in terms of spatio-temporal decorrelation. Secondly, we propose refined motion estimation via efficient registration prior to inter-prediction, which searches the temporal correspondence between adjacent frames of irregular point clouds. Finally, we present a complete framework based on the optimal inter-coding and our previously proposed intra-coding, where we determine the optimal coding mode from rate-distortion optimization with the proposed offline-trained lambda-Q model. Experimental results show that we achieve around 17% bit rate reduction on average over competitive dynamic point cloud compression methods.
Compression of point clouds has so far been confined to coding the positions of a discrete set of points in space and the attributes of those discrete points. We introduce an alternative approach based on volumetric f...
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Compression of point clouds has so far been confined to coding the positions of a discrete set of points in space and the attributes of those discrete points. We introduce an alternative approach based on volumetric functions that are functions defined not just on a finite set of points but throughout space. As in regression analysis, volumetric functions are continuous functions that are able to interpolate values on a finite set of points as linear combinations of continuous basis functions. Using a B-spline wavelet basis, we are able to code volumetric functions representing both geometry and attributes. Geometry compression is addressed in Part II of this paper, while attribute compression is addressed in Part I. attributes are represented by a volumetric function whose coefficients can be regarded as a critically sampled orthonormal transform that generalizes the recent successful Region-Adaptive Hierarchical (or Haar) Transform to higher orders. Experimental results show that attribute compression using higher order volumetric functions is an improvement over the first-order functions used in the emerging MPEG point cloud compression standard.
Point clouds, crucial for representing 3D objects and scenes, offer immersive and precise depictions of the real world. Despite their superiority, the substantial data volume challenges current multimedia ecosystems. ...
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ISBN:
(纸本)9798350358483;9798350358490
Point clouds, crucial for representing 3D objects and scenes, offer immersive and precise depictions of the real world. Despite their superiority, the substantial data volume challenges current multimedia ecosystems. To address this, the Moving Picture Expert Group (MPEG) initiated the point cloud compression project in 2017, leading to two branches: Video-based Point Cloud Compression (V-PCC) and Geometry-based Point Cloud Compression (G-PCC). The first edition of G-PCC was published in March 2023, and ongoing efforts over the past three years have advanced towards G-PCC Edition 2. This paper aims to present recent technical achievements and the current status of G-PCC standardization activities.
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