Contextual Homogeneity-Based Patch Decomposition Method for Higher Point Cloud Compression

Rhyu, Sungryeul; Kim, Junsik; Im, Jungho; Kim, Kyuheon

doi:10.1109/access.2020.3038800

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…Li et al [29] have proposed a method for decreasing unoccupied pixels among different patches due to the inefficiency of coding unused space during video compression. Rhyl et al [30] proposed their method for obtaining a contextual homogeneity-based patch decomposition affecting compression efficiency. However, it does not work on additional attributes such as reflection and material.…”

Section: Estimate Normalsmentioning

confidence: 99%

Improved Patch Packing and Refining Segmentation for the V-PCC Standard

Luo,

Chi,

et al. 2024

Applied Sciences

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High-performance coding solutions are urgently needed for the storage and transmission of 3D point clouds due to the development of 3D data acquisition facilities and the increasing scale of acquired point clouds. Video-based point cloud compression (V-PCC) is the most advanced international standard for compressing dynamic point clouds. However, it still has serious issues of time consumption and the large size of the occupancy map. Considering the aforementioned issues, based on V-PCC, we propose the Voxel Selection-based Refining Segmentation (VS-RS), which is used to accelerate the refining segmentation process of the point cloud. Furthermore, the data-adaptive patch packing (DAPP) is proposed to reduce the size of the occupancy map. In order to specify the effect of the improvement, we also designed novel evaluation indicators. Experimental results show that the proposed method achieves a Bjøntegaard Delta rate (BD-rate) gain of −1.58% in the V-PCC benchmark. Additionally, it reduces encoding time by up to 31.86% and reduces the size of the occupancy map by up to 20.14%.

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Section: Estimate Normalsmentioning

confidence: 99%

Improved Patch Packing and Refining Segmentation for the V-PCC Standard

Luo,

Chi,

et al. 2024

Applied Sciences

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“…To enhance the V-PCC standard's rate-distortion (RD) performance, Costa et al, in 43 45 suggested a contextual homogeneity-based patch decomposing. Their approach eliminates the possibility of a single patch having many contextual regions in shape and colour.…”

Section: Enhancing V-pcc By Reducing Unused Spaces In 2d Mapsmentioning

confidence: 99%

“…However, a rebuilt point cloud's geometric quality falls short of the V-PCC method. The approach in 45 recognises that many points may be overlooked due to the space between the inner and outer surfaces of the point cloud exceeding the limitation since the distance of the closest and farthest 3D points cannot surpass the predefined range boundary. The authors suggest creating a new patch to address the remaining points.…”

Section: Enhancing V-pcc Projection Layer Efficiencymentioning

confidence: 99%

Improved video-based point cloud compression via segmentation

Tohidi,

Paul,

Ulhaq

et al. 2024

Preprint

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Point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such as virtual realityand augmented reality. However, the point cloud, especially the dynamic one, must be compressed efficiently due to its hugedata volume. The latest video-based point cloud compression (V-PCC) standard for dynamic point clouds divides the 3D pointcloud into many patches using computationally expensive normal estimation, segmentation, and refinement. The patches areprojected onto a 2D plane to apply existing video coding techniques. This process often results in losing proximity informationand some original points. This loss induces artefacts that adversely affect user perception. The proposed method segments dynamic point clouds based on shape similarity and occlusion before patch generation. This segmentation strategy helps tomaintain the proximity of the points and retain more original points by exploiting density and occlusion of the points . The experimental results establish that the proposed method significantly outperforms the V-PCC standard and other relevant methods in terms of rate-distortion performance and subjective quality testing for both geometric and texture data of several benchmark video sequences.

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“…In viewing the V-PCC common test conditions (CTCs) [3], each 3D point cloud frame consists of 800,000-2,900,000 3D points, and a 3D point is stored with 10 bits to represent the geometry information and 8 bits for color (RGB) information. A maximum of 4.3 Gbps bandwidth is required to transmit such a point cloud sequence with a frame rate of 30 fps, and it is challenging in the current network environment [4]. Therefore, efficient point cloud compression (PCC) technologies are indispensable.…”

Section: Introductionmentioning

confidence: 99%

Fast Grid-Based Refining Segmentation Method in Video-Based Point Cloud Compression

Kim

2021

IEEE Access

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The video-based point cloud compression (V-PCC, ISO/IEC 23090-5) is the state-of-theart international standard for compressing dynamic point clouds developed by the moving picture experts group (MPEG). It has been achieved good rate-distortion (RD) performance by employing the 2D-based dynamic point cloud compression. As a brief look, V-PCC first converts the 3D input point cloud into a set of 2D patches followed by a packing process. The packing process then maps the patches into a 2D grid. Such a way allows compressing the patches utilizing the existing video coding standards. Besides the RD performance, complexity is another vital factor to consider in performance evaluations. In the V-PCC encoder, the self-time accounts for on average 15.9% and a maximum of 48.2% of the total-time, which can be a hindrance to realizing real-time V-PCC applications. One of the most computationally intensive modules of V-PCC is the grid-based refining segmentation (G-RS). Thus this paper proposes a fast G-RS method that can adaptively select voxels that need the refining segmentation. More concretely, the proposed method classifies the voxels based on the projection plane indices of 3D points and only applies the refining process to the selected voxels. Experimental results demonstrate that the proposed method reduces the complexity of the refining steps in G-RS, on average, by 60.7% and 62.5% without coding efficiency loss compared to the test model for category 2 (TMC2) version 12.0 reference software under the random access (RA) and all-intra (AI) configurations, respectively.INDEX TERMS video-based point cloud compression, dynamic point cloud, fast algorithms, low-complex video encoders, voxel-based refining segmentation, encoder optimization

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Contextual Homogeneity-Based Patch Decomposition Method for Higher Point Cloud Compression

Cited by 9 publications

References 15 publications

Improved Patch Packing and Refining Segmentation for the V-PCC Standard

Improved Patch Packing and Refining Segmentation for the V-PCC Standard

Improved video-based point cloud compression via segmentation

Fast Grid-Based Refining Segmentation Method in Video-Based Point Cloud Compression

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