To improve the distance-based level of detail(LoD)construction in point cloud compression
a prediction residual-based LoD optimization model and a fast LoD generation method are proposed. The relationship between LoD prediction residuals and coding bitrate is firstly deduced. A model of LoD prediction residual and distance is further constructed
and the model parameters can be obtained by pre-coding or online calculation of the point cloud. Relying on this model
the optimal number of LoD layers can be obtained by minimizing the prediction residuals. To lower the complexity of the proposed method
the influences of the model parameters on the coding performance are analyzed. It is found that the number of points in detail layer decreases exponentially with the increasing LoD layers
which makes the impact on coding performance weaken sharply. Following this analysis
the model parameter determination is simplified according to uniform sampling and smooth distribution features of point clouds. The optimal number of LoD layers can be obtained by the proportion of the points in the detail layer to the total points in the point cloud
so as to achieve the optimal coding performance. Furthermore
a fast LoD generation method based on threshold control is proposed to heighten the model practicability. Experimental results show that the proposed method enables to shorten encoding time by 4% and decoding time by 6% without any performance loss.
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references
MEKURIA R, BLOM K, CESAR P. Design, implementation, and evaluation of a point cloud codec for tele-immersive video [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(4): 828-842.
LI Houqiang, LI Li, LI Zhu. A review of point cloud compression [J]. ZTE Technology Journal, 2021, 27(1): 5-9.
ZHANG Cha, FLORÊNCIO D, LOOP C. Point cloud attribute compression with graph transform [C]∥Proceedings of the 2014 IEEE International Conference on Image Processing(ICIP). Piscataway, NJ, USA: IEEE, 2014: 2066-2070.
COHEN R A, TIAN Dong, VETRO A. Point cloud attribute compression using 3-D intra prediction and shape-adaptive transforms [C]∥Proceedings of the 2016 Data Compression Conference(DCC). Piscataway, NJ, USA: IEEE, 2016: 141-150.
COHEN R A, TIAN Dong, VETRO A. Attribute compression for sparse point clouds using graph transforms [C]∥Proceedings of the 2016 IEEE International Conference on Image Processing(ICIP). Piscataway, NJ, USA: IEEE, 2016: 1374-1378.
DE QUEIROZ R L, CHOU P A. Compression of 3D point clouds using a region-adaptive hierarchical transform [J]. IEEE Transactions on Image Processing, 2016, 25(8): 3947-3956.
CLARK J H. Hierarchical geometric models for visible-surface algorithms [J]. ACM SIGGRAPH Computer Graphics, 1976, 10(2): 267.
KATHARIYA B, ZAKHARCHENKO V, LI Zhu, et al. Level-of-detail generation using binary-tree for lifting scheme in LiDAR point cloud attributes coding [C]∥Proceedings of the 2019 Data Compression Conference(DCC). Piscataway, NJ, USA: IEEE, 2019: 580.
FAN Yuxue, HUANG Yan, PENG Jingliang. Point cloud compression based on hierarchical point clustering [C]∥Proceedings of the 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. Piscataway, NJ, USA: IEEE, 2013: 1-7.
LUEBKE D, REDDY M, COHEN J D, et al. Level of detail for 3D graphics [M]. San Francisco, CA, USA: Morgan Kaufmann Publisher, 2002: 19-45.
CHOU P A, KOROTEEV M, KRIVOKUCA M. A volumetric approach to point cloud compression: part I Attribute compression [J]. IEEE Transactions on Image Processing, 2020, 29: 2203-2216.
SCHWARZ S, PREDA M, BARONCINI V, et al. Emerging MPEG standards for point cloud compression [J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2019, 9(1): 133-148.
周凡. 点云属性压缩算法研究 [D]. 西安: 西安电子科技大学, 2019: 51-63.
MAMMOU K, KIM J, VALENTIN V, et al. Efficient implementation of the lifting scheme in TMC13 [EB/OL]. [2021-03-01]. https: ∥dms.mpeg.expert /doc_end_user/current_document.php?id=63053id_meeting=0.
VALENTIN V, MAMMOU K, TOURAPIS A, et al. Improved G-PCC lossless and near-lossless coding [EB/OL]. [2021-03-01]. https: ∥dms.mpeg.expert /doc_end_user/current_document.php?id=64363id_meeting=0.
MPEG 3DG. Common test conditions for G-PCC [EB/OL]. [2021-03-01]. https: ∥dms.mpeg.expert /doc_end_user/current_document.php?id=76939id_meeting=0.
DE QUEIROZ R L, CHOU P A. Transform coding for point clouds using a Gaussian process model [J]. IEEE Transactions on Image Processing, 2017, 26(7): 3507-3517.
SANDRI G, DE QUEIROZ R L, CHOU P A. Comments on “compression of 3D point clouds using a region-adaptive hierarchical transform” [EB/OL]. [2021-03-10]. https:∥arxiv. org/abs/1805.09146.