Learning-based Lossless Point Cloud Geometry Coding using Sparse Tensors

Nguyen DT, Kaup A (2022)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2022

Conference Proceedings Title: IEEE International Conference on Image Processing ICIP 2022

Event location: Bordeaux, France FR

URI: https://arxiv.org/abs/2204.05043

DOI: 10.1109/icip46576.2022.9897827

Abstract

Most point cloud compression methods operate in the voxel or octree domain which is not the original representation of point clouds. Those representations either remove the geometric information or require high computational power for processing. In this paper, we propose a context-based lossless point cloud geometry compression that directly processes the point representation. Operating on a point representation allows us to preserve geometry correlation between points and thus to obtain an accurate context model while significantly reduce the computational cost. Specifically, our method uses a sparse convolution neural network to estimate the voxel occupancy sequentially from the x,y,z input data. Experimental results show that our method outperforms the state-of-the-art geometry compression standard from MPEG with average rate savings of 52% on a diverse set of point clouds from four different datasets.

Authors with CRIS profile

Dat Thanh Nguyen Lehrstuhl für Multimediakommunikation und Signalverarbeitung Andre Kaup Lehrstuhl für Multimediakommunikation und Signalverarbeitung

How to cite

APA:

Nguyen, D.T., & Kaup, A. (2022). Learning-based Lossless Point Cloud Geometry Coding using Sparse Tensors. In IEEE International Conference on Image Processing ICIP 2022. Bordeaux, France, FR.

MLA:

Nguyen, Dat Thanh, and André Kaup. "Learning-based Lossless Point Cloud Geometry Coding using Sparse Tensors." Proceedings of the IEEE International Conference on Image Processing (ICIP), Bordeaux, France 2022.

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