Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting

Xu, Yingzhan; Zhu, Wen-Hong; Xu, Yiling; Li, Zhu

doi:10.1109/icassp.2019.8682413

Cited by 14 publications

(3 citation statements)

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“…Due to the functionalities and optimizations accumulated in video codecs, there are many improvements in the literature for video-based PCC [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Sevom et al [ 24 ] reduced the encoding and decoding complexity by dropping secondary texture and geometry content from the processing chain and introducing the geometry-guided 3D data interpolation.…”

Section: Introductionmentioning

confidence: 99%

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A Method Based on Curvature and Hierarchical Strategy for Dynamic Point Cloud Compression in Augmented and Virtual Reality System

Sun

Yan

et al. 2022

Sensors

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As a kind of information-intensive 3D representation, point cloud rapidly develops in immersive applications, which has also sparked new attention in point cloud compression. The most popular dynamic methods ignore the characteristics of point clouds and use an exhaustive neighborhood search, which seriously impacts the encoder’s runtime. Therefore, we propose an improved compression means for dynamic point cloud based on curvature estimation and hierarchical strategy to meet the demands in real-world scenarios. This method includes initial segmentation derived from the similarity between normals, curvature-based hierarchical refining process for iterating, and image generation and video compression technology based on de-redundancy without performance loss. The curvature-based hierarchical refining module divides the voxel point cloud into high-curvature points and low-curvature points and optimizes the initial clusters hierarchically. The experimental results show that our method achieved improved compression performance and faster runtime than traditional video-based dynamic point cloud compression.

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Section: Introductionmentioning

confidence: 99%

“…Li et al [ 25 ] used reconstructed geometric information to improve the coding efficiency of attribute videos in the dynamic PCC. In the field of geometric compression, Xu et al [ 26 ] conducted the geometry PCC via patch-wise polynomial fitting. However, this method was not satisfactory at high bit rates.…”

Section: Introductionmentioning

confidence: 99%

A Method Based on Curvature and Hierarchical Strategy for Dynamic Point Cloud Compression in Augmented and Virtual Reality System

Sun

Yan

et al. 2022

Sensors

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“…Therefore, it is necessary to convert point clouds into normalized and organized data structures, such as volume model, tree structure, grid model and a multi-view image. Hence using the traditional methods, the encoding techniques based on tree structure [ 8 , 9 , 10 , 11 , 12 , 13 ], surface approximation [ 13 , 14 ] and mapping [ 15 , 16 , 17 , 18 , 19 ], are several representative encoding schemes of point cloud geometry. The existing standard compression methods for point clouds are released by MPEG [ 1 ] consisting of geometry-based PCC (G-PCC) applied to static point clouds and video-based PCC (V-PCC) applied to dynamic point clouds, which are both typical methods.…”

Section: Introductionmentioning

confidence: 99%

Point Cloud Geometry Compression Based on Multi-Layer Residual Structure

Wang

Sun

et al. 2022

Entropy

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Point cloud data are extensively used in various applications, such as autonomous driving and augmented reality since it can provide both detailed and realistic depictions of 3D scenes or objects. Meanwhile, 3D point clouds generally occupy a large amount of storage space that is a big burden for efficient communication. However, it is difficult to efficiently compress such sparse, disordered, non-uniform and high dimensional data. Therefore, this work proposes a novel deep-learning framework for point cloud geometric compression based on an autoencoder architecture. Specifically, a multi-layer residual module is designed on a sparse convolution-based autoencoders that progressively down-samples the input point clouds and reconstructs the point clouds in a hierarchically way. It effectively constrains the accuracy of the sampling process at the encoder side, which significantly preserves the feature information with a decrease in the data volume. Compared with the state-of-the-art geometry-based point cloud compression (G-PCC) schemes, our approach obtains more than 70–90% BD-Rate gain on an object point cloud dataset and achieves a better point cloud reconstruction quality. Additionally, compared to the state-of-the-art PCGCv2, we achieve an average gain of about 10% in BD-Rate.

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