Learning-Based Lossless Compression of 3D Point Cloud Geometry

Nguyen, Dat Thanh; Quach, Maurice; Valenzise, Giuseppe; Duhamel, Pierre

doi:10.1109/icassp39728.2021.9414763

Cited by 45 publications

(35 citation statements)

References 13 publications

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“…to perform lossless compression. We compare our method with the hand-crafted inter-frame octree-based contexts model P(full) (Garcia et al 2019), state-of-the-art compression method VoxelDNN (Nguyen et al 2021a) and its fast version MSVoxelDNN (Nguyen et al 2021b). We set the training condition following VoxelDNN and test the models on different depth data.…”

Section: Methodsmentioning

confidence: 99%

OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression

Fu,

Li,

Song

et al. 2022

Preprint

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In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution. However, the contexts gathered by the previous voxel-based methods decrease when handling sparse point clouds. To address this problem, we propose a multiple-contexts deep learning framework called OctAttention employing the octree structure, a memory-efficient representation for point clouds. Our approach encodes octree symbol sequences in a lossless way by gathering the information of sibling and ancestor nodes. Expressly, we first represent point clouds with octree to reduce spatial redundancy, which is robust for point clouds with different resolutions. We then design a conditional entropy model with a large receptive field that models the sibling and ancestor contexts to exploit the strong dependency among the neighboring nodes and employ an attention mechanism to emphasize the correlated nodes in the context. Furthermore, we introduce a mask operation during training and testing to make a trade-off between encoding time and performance. Compared to the previous state-of-the-art works, our approach obtains a 10%-35% BD-Rate gain on the LiDAR benchmark (e.g. SemanticKITTI) and object point cloud dataset (e.g. MPEG 8i, MVUB), and saves 95% coding time compared to the voxel-based baseline. The code is available at https://github.com/zb12138/OctAttention.

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Section: Methodsmentioning

confidence: 99%

OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression

Fu,

Li,

Song

et al. 2022

Preprint

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“…By introducing a sequential dependency in the voxel grid, one can use voxels at the current LoD as context. Nguyen et al (2021) proposes a deep CNN with masked convolutions called VoxelDNN for lossless compression of point cloud geometry. The neural network predicts the occupancy probability of each voxel, and the probabilities are then fed to an arithmetic coder.…”

Section: Lossless Compressionmentioning

confidence: 99%

Survey on Deep Learning-Based Point Cloud Compression

Quach¹,

Pang²,

Tian³

et al. 2022

Front. Signal Process.

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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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“…These architectures are insufficient for the processing of large point cloud data. VoxelDNN was proposed in [ 38 ] which combines the octree and voxel domains. Inference in this lossless compression is slow, and the occupancy probabilities are predicted sequentially, voxel by voxel, while the improved MSVoxelDNN models voxel occupancy and achieves rate savings over G-PCC up to 17% on average [ 39 ].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Point Cloud Compression Based on Projections, Surface Reconstruction and Video Compression

Dumic

Bjelopera

Nüchter

2021

Sensors

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In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video compression is specific for geometry (FFV1) and texture (H.265/HEVC). Decompressed point clouds are reconstructed using a Poisson surface reconstruction algorithm. Comparison with the original point clouds was performed using point-to-point and point-to-plane measures. Comprehensive experiments show better performance for some projection maps: cylindrical, Miller and Mercator projections.

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Learning-Based Lossless Compression of 3D Point Cloud Geometry

Cited by 45 publications

References 13 publications

OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression

OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression

Survey on Deep Learning-Based Point Cloud Compression

Dynamic Point Cloud Compression Based on Projections, Surface Reconstruction and Video Compression

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