Robust Multi-generation Learned Compression of Point Cloud Attribute
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916821178777600 |
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| author | Liu, Xiangzuo Liu, Zhikai Yu, PengPeng Huang, Ruishan Liang, Fan |
| author_facet | Liu, Xiangzuo Liu, Zhikai Yu, PengPeng Huang, Ruishan Liang, Fan |
| contents | Existing learned point cloud attribute compression methods primarily focus on single-pass rate-distortion optimization, while overlooking the issue of cumulative distortion in multi-generation compression scenarios. This paper, for the first time, investigates the multi-generation issue in learned point cloud attribute compression. We identify two primary factors contributing to quality degradation in multi-generation compression: quantization-induced non-idempotency and transformation irreversibility. To address the former, we propose a Mapping Idempotency Constraint, that enables the network to learn the complete compression-decompression mapping, enhancing its robustness to repeated processes. To address the latter, we introduce a Transformation Reversibility Constraint, which preserves reversible information flow via a quantization-free training path. Further, we propose a Latent Variable Consistency Constraint which enhances the multi-generation compression robustness by incorporating a decompression-compression cross-generation path and a latent variable consistency loss term. Extensive experiments conducted on the Owlii and 8iVFB datasets verify that the proposed methods can effectively suppress multi-generation loss while maintaining single-pass rate-distortion performance comparable to baseline models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01320 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Multi-generation Learned Compression of Point Cloud Attribute Liu, Xiangzuo Liu, Zhikai Yu, PengPeng Huang, Ruishan Liang, Fan Multimedia Existing learned point cloud attribute compression methods primarily focus on single-pass rate-distortion optimization, while overlooking the issue of cumulative distortion in multi-generation compression scenarios. This paper, for the first time, investigates the multi-generation issue in learned point cloud attribute compression. We identify two primary factors contributing to quality degradation in multi-generation compression: quantization-induced non-idempotency and transformation irreversibility. To address the former, we propose a Mapping Idempotency Constraint, that enables the network to learn the complete compression-decompression mapping, enhancing its robustness to repeated processes. To address the latter, we introduce a Transformation Reversibility Constraint, which preserves reversible information flow via a quantization-free training path. Further, we propose a Latent Variable Consistency Constraint which enhances the multi-generation compression robustness by incorporating a decompression-compression cross-generation path and a latent variable consistency loss term. Extensive experiments conducted on the Owlii and 8iVFB datasets verify that the proposed methods can effectively suppress multi-generation loss while maintaining single-pass rate-distortion performance comparable to baseline models. |
| title | Robust Multi-generation Learned Compression of Point Cloud Attribute |
| topic | Multimedia |
| url | https://arxiv.org/abs/2507.01320 |