Folding-Based Compression Of Point Cloud Attributes

Quach, Maurice; Valenzise, Giuseppe; Dufaux, Frédéric

doi:10.1109/icip40778.2020.9191180

Cited by 42 publications

(26 citation statements)

References 3 publications

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“…This is similar to the concept of UV texture maps (Catmull, 1974) in Computer Graphics except that here we seek to recover such a parameterization from a point cloud. In this context, Quach et al (2020a) propose a folding based approach for point cloud compression illustrated in Figure 7. FoldingNet (Yang et al, 2017) reconstructs a point cloud by folding a 2D plane using an autoencoder neural network.…”

Section: Deep Learning Based Attribute Compressionmentioning

confidence: 99%

“…Deep learning based methods handle the irregularity of the geometry by using a 3D regular space (voxel grid) (Alexiou et al, 2020), by mapping attributes onto a 2D grid (Quach et al, 2020a) or with the use of point convolutions to define CNNs that operate directly on the points (Sheng et al, 2021). Note that such point convolutions can often be seen as graph convolutions with the topology of the graph built from the point cloud geometry and its neighborhood structure.…”

Section: D Approachesmentioning

confidence: 99%

“…Numerous approaches have explored the use of 2D images for point cloud attribute compression (Mekuria et al, 2017;Zhang et al, 2017;MPEG, 2020b). Specifically, Quach et al (2020a) has explored a deep learning based approach for mapping attributes from each point to a 2D image using a FoldingNet (Yang et al, 2017). This presents the advantage of enabling the use of any image processing or image compression method.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

“…Currently, deep learning based attribute compression approaches (Alexiou et al, 2020;Quach et al, 2020a;Isik et al, 2021;Sheng et al, 2021) are not yet competitive with the latest G-PCC codec. However, it is shown in Isik et al (2021) and Sheng et al (2021) that these approaches are actually competitive with RAHT which is used in G-PCC but improved with additional coding tools such as predictive RAHT, context adaptive arithmetic coding and joint entropy coding of color as discussed in Isik et al (2021).…”

Section: Compression Performancementioning

confidence: 99%

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Survey on Deep Learning-Based Point Cloud Compression

Quach¹,

Pang²,

Tian³

et al. 2022

Front. Signal Process.

Self Cite

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Point clouds are becoming essential in key applications with advances in capture technologies leading to large volumes of data. Compression is thus essential for storage and transmission. In this work, the state of the art for geometry and attribute compression methods with a focus on deep learning based approaches is reviewed. The challenges faced when compressing geometry and attributes are considered, with an analysis of the current approaches to address them, their limitations and the relations between deep learning and traditional ones. Current open questions in point cloud compression, existing solutions and perspectives are identified and discussed. Finally, the link between existing point cloud compression research and research problems to relevant areas of adjacent fields, such as rendering in computer graphics, mesh compression and point cloud quality assessment, is highlighted.

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Section: Deep Learning Based Attribute Compressionmentioning

confidence: 99%

Section: D Approachesmentioning

confidence: 99%

Section: Dimensionality Reductionmentioning

confidence: 99%

Section: Compression Performancementioning

confidence: 99%

See 2 more Smart Citations

Survey on Deep Learning-Based Point Cloud Compression

Quach¹,

Pang²,

Tian³

et al. 2022

Front. Signal Process.

Self Cite

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“…In [16], a deeper auto-encoding architecture is proposed, based on 3D convolution layers stacked with Voxception-ResNet structures and a hyper-prior. In [17], an encoding scheme relying on folding of a 2D grid onto a point cloud is proposed, with the attributes of the latter being mapped on top of it. In [18], geometry and color information is encoded directly in the 3D domain by extracting features from regular grids, using 3D convolutions and capturing spatial redundancies.…”

Section: Related Workmentioning

confidence: 99%

On Block Prediction For Learning-Based Point Cloud Compression

Lazzarotto

Alexiou

Ebrahimi

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Point clouds are among popular visual representations for immersive media. However, the vast amount of information generated during their acquisition requires effective compression for practical applications. Although relevant activities from standardization bodies have led to state-of-the-art compression using conventional methods, learning-based encoders have recently emerged as promising solutions with comparable performance while offering additional attractive features. Yet, there is still a large unexplored space for research that can lead to further advances. In this paper, we propose a block prediction module for bit-rate reduction of geometryonly point clouds. Our method exploits spatial redundancies at the decoding stage between block partitions in the point cloud, and predicts a query block using Generative Adversarial Networks. Results show performance improvements of the objective metrics at low bit-rates, after integration in a baseline auto-encoder architecture.

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