Point Cloud Coding: Adopting a Deep Learning-based Approach

Guarda, Andre F. R.; Rodrigues, Nuno M. M.; Pereira, Fernando

doi:10.1109/pcs48520.2019.8954537

Cited by 49 publications

(25 citation statements)

References 11 publications

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“…Inspired by the success in learning-based image compression, deep learning has been recently adopted in point cloud coding methods [12][13][14][15][16]. The proposed methods in [15,16] encode each 64 × 64 × 64 sub-block of PC using a 3D convolutional auto-encoder.…”

Section: Related Workmentioning

confidence: 99%

Learning-Based Lossless Compression of 3D Point Cloud Geometry

Nguyen

Quach

Valenzise

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper presents a learning-based, lossless compression method for static point cloud geometry, based on contextadaptive arithmetic coding. Unlike most existing methods working in the octree domain, our encoder operates in a hybrid mode, mixing octree and voxel-based coding. We adaptively partition the point cloud into multi-resolution voxel blocks according to the point cloud structure, and use octree to signal the partitioning. On the one hand, octree representation can eliminate the sparsity in the point cloud. On the other hand, in the voxel domain, convolutions can be naturally expressed, and geometric information (i.e., planes, surfaces, etc.) is explicitly processed by a neural network. Our context model benefits from these properties and learns a probability distribution of the voxels using a deep convolutional neural network with masked filters, called VoxelDNN. Experiments show that our method outperforms the state-of-the-art MPEG G-PCC standard with average rate savings of 28% on a diverse set of point clouds from the Microsoft Voxelized Upper Bodies (MVUB) and MPEG.

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Section: Related Workmentioning

confidence: 99%

Learning-Based Lossless Compression of 3D Point Cloud Geometry

Nguyen

Quach

Valenzise

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Recently, deep learning has been widely applied in point cloud coding in both the octree domain [11], [17] and especially voxel domain [1], [18]- [20]. A coding method for static LiDAR point cloud is proposed in [11], which learns the probability distributions of the octree based on contextual information and uses an arithmetic coder for lossless coding.…”

Section: Related Workmentioning

confidence: 99%

Multiscale deep context modeling for lossless point cloud geometry compression

Nguyen

Quach

Valenzise

et al. 2021

2021 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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We propose a practical deep generative approach for lossless point cloud geometry compression, called MSVoxelDNN, and show that it significantly reduces the rate compared to the MPEG G-PCC codec. Our previous work based on autoregressive models (VoxelDNN [1]) has a fast training phase, however, inference is slow as the occupancy probabilities are predicted sequentially, voxel by voxel. In this work, we employ a multiscale architecture which models voxel occupancy in coarse-to-fine order. At each scale, MSVoxelDNN divides voxels into eight conditionally independent groups, thus requiring a single network evaluation per group instead of one per voxel. We evaluate the performance of MSVoxelDNN on a set of point clouds from Microsoft Voxelized Upper Bodies (MVUB) and MPEG, showing that the current method speeds up encoding/decoding times significantly compared to the previous VoxelDNN, while having average rate saving over G-PCC of 17.5%

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“…Deep learning-based approaches commonly exploit autoencoding architectures that target compression of geometryonly information in a block-by-block basis. Two early studies are presented in [11,12], using shallow architectures composed of convolution and de-convolution layers for analysis and synthesis. The impact of several parameters added to the initial version of the former network is evaluated in [13].…”

Section: Related Workmentioning

confidence: 99%

“…The impact of several parameters added to the initial version of the former network is evaluated in [13]. In [14], a rate-distortion performance analysis is conducted on the latent space of [12], which is enriched with a hyper-prior and the possibility of explicit quantization in [15]. In [16], a deeper auto-encoding architecture is proposed, based on 3D convolution layers stacked with Voxception-ResNet structures and a hyper-prior.…”

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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Point Cloud Coding: Adopting a Deep Learning-based Approach

Cited by 49 publications

References 11 publications

Learning-Based Lossless Compression of 3D Point Cloud Geometry

Learning-Based Lossless Compression of 3D Point Cloud Geometry

Multiscale deep context modeling for lossless point cloud geometry compression

On Block Prediction For Learning-Based Point Cloud Compression

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