Denoising and Inpainting for Point Clouds Compressed by V-PCC

Cao, Keming; Cosman, Pamela C.

doi:10.1109/access.2021.3102029

Cited by 15 publications

(3 citation statements)

References 40 publications

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“…On the other hand, G-PCC directly encodes content in 3D space, making it suitable for sparse point clouds, such as those from Light Detection and Ranging (LiDAR) sensors and applications like heritage preservation [135]. A growing number of research articles have delved into point cloud compression, proposing new methods or enhancements to both V-PCC and G-PCC [138,139]. Others have analyzed and discussed various compression approaches and tools within V-PCC and G-PCC [140].…”

Section: Point Cloud Compressionmentioning

confidence: 99%

A Survey on Video Streaming for Next-Generation Vehicular Networks

Huang,

Cheng,

Lien

et al. 2024

Electronics

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As assisted driving technology advances and vehicle entertainment systems rapidly develop, future vehicles will become mobile cinemas, where passengers can use various multimedia applications in the car. In recent years, the progress in multimedia technology has given rise to immersive video experiences. In addition to conventional 2D videos, 360° videos are gaining popularity, and volumetric videos, which can offer users a better immersive experience, have been discussed. However, these applications place high demands on network capabilities, leading to a dependence on next-generation wireless communication technology to address network bottlenecks. Therefore, this study provides an exhaustive overview of the latest advancements in video streaming over vehicular networks. First, we introduce related work and background knowledge, and provide an overview of recent developments in vehicular networking and video types. Next, we detail various video processing technologies, including the latest released standards. Detailed explanations are provided for network strategies and wireless communication technologies that can optimize video transmission in vehicular networks, paying special attention to the relevant literature regarding the current development of 6G technology that is applied to vehicle communication. Finally, we proposed future research directions and challenges. Building upon the technologies introduced in this paper and considering diverse applications, we suggest a suitable vehicular network architecture for next-generation video transmission.

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Section: Point Cloud Compressionmentioning

confidence: 99%

A Survey on Video Streaming for Next-Generation Vehicular Networks

Huang,

Cheng,

Lien

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…The abovementioned problems lead to various artifacts in a V-PCC reconstructed point cloud, especially when high quantisation parameters (QP) are used [48][49][50] . Cao and Cosman classified different geometric compression artifacts 51 and provided a detection and removal technique for each artifact. Wei et al's deep learning-based artifact removal is another proposal made in 52 in 2021.…”

Section: Post-processing To Eliminate Artefacts In V-pccmentioning

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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“…Lozes et al [9] proposed a repair algorithm for CPC, which refers to the neighborhood near the hole area to calculate the geometric structure and color information, and optimizes the repair results through PDE. Cao et al [10] assigned the color attribute of the correction point of the CPC by using the color of the point closest to the point.…”

Section: Introductionmentioning

confidence: 99%

Point cloud projection based light-to-medium G-PCC-1 hole distortion repair method for colored point cloud

Tao¹,

Jiang²,

Yu³

et al. 2023

Optoelectronic Imaging and Multimedia Technology IX

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With the wide application of Colored Point Clouds(CPC), the amount of data is increasing, and efficient compression is required in practical applications. The most advanced compression technology for static CPC is the G-PCC proposed by MPEG. However, quantification errors from G-PCC-1 (Octree) can cause grid hole distortion, which can have a serious impact on the quality of user's visual perception. For this reason, this paper proposes a Point Cloud Projection based light-to-medium G-PCC-1 Hole Distortion Repair method (denoted as P-GHDR) for CPC. The distorted CPC is projected from 3D space to 2D plane, and the G-PCC-1 distorted is repaired by combining multi-view color and geometric projection maps. Finally, the repaired CPC is reconstructed by reverse projection. Experiments show that the proposed method can effectively improve the geometric and visual objective metrics of G-PCC-1 coded CPC, and can significantly improve the quality of CPC reconstructed with light-to-medium G-PCC-1 codes.

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Denoising and Inpainting for Point Clouds Compressed by V-PCC

Cited by 15 publications

References 40 publications

A Survey on Video Streaming for Next-Generation Vehicular Networks

A Survey on Video Streaming for Next-Generation Vehicular Networks

Improved video-based point cloud compression via segmentation

Point cloud projection based light-to-medium G-PCC-1 hole distortion repair method for colored point cloud

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