{"ID":2898990,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.01320","arxiv_id":"2507.01320","title":"Robust Multi-generation Learned Compression of Point Cloud Attribute","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2507.01320","url_pdf":"https://arxiv.org/pdf/2507.01320v1","authors":"[\"Xiangzuo Liu\",\"Zhikai Liu\",\"PengPeng Yu\",\"Ruishan Huang\",\"Fan Liang\"]","published":"2025-07-02T03:08:37Z","proceeding":"cs.MM","tasks":"[\"cs.MM\"]","methods":"[]","has_code":false}