END-TO-END LEARNED LOSSY DYNAMIC POINT CLOUD ATTRIBUTE COMPRESSION

Nguyen DT, Zieger D, Stamminger M, Kaup A (2024)

Publication Type: Conference contribution

Publication year: 2024

Journal

Publisher: IEEE Computer Society

Pages Range: 3355-3360

Conference Proceedings Title: Proceedings - International Conference on Image Processing, ICIP

Event location: Abu Dhabi, ARE

ISBN: 9798350349399

DOI: 10.1109/ICIP51287.2024.10648044

Abstract

Recent advancements in point cloud compression have primarily emphasized geometry compression while comparatively fewer efforts have been dedicated to attribute compression. This study introduces an end-to-end learned dynamic lossy attribute coding approach, utilizing an efficient high-dimensional convolution to capture extensive inter-point dependencies. This enables the efficient projection of attribute features into latent variables. Subsequently, we employ a context model that leverage previous latent space in conjunction with an auto-regressive context model for encoding the latent tensor into a bitstream. Evaluation of our method on widely utilized point cloud datasets from the MPEG and Microsoft demonstrates its superior performance compared to the core attribute compression module Region-Adaptive Hierarchical Transform method from MPEG Geometry Point Cloud Compression with 38.1% Bjontegaard Delta-rate saving in average while ensuring a low-complexity encoding/decoding.

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How to cite

APA:

Nguyen, D.T., Zieger, D., Stamminger, M., & Kaup, A. (2024). END-TO-END LEARNED LOSSY DYNAMIC POINT CLOUD ATTRIBUTE COMPRESSION. In Proceedings - International Conference on Image Processing, ICIP (pp. 3355-3360). Abu Dhabi, ARE: IEEE Computer Society.

MLA:

Nguyen, Dat Thanh, et al. "END-TO-END LEARNED LOSSY DYNAMIC POINT CLOUD ATTRIBUTE COMPRESSION." Proceedings of the 31st IEEE International Conference on Image Processing, ICIP 2024, Abu Dhabi, ARE IEEE Computer Society, 2024. 3355-3360.

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