UGAE: Unified Geometry and Attribute Enhancement for G-PCC Compressed Point Clouds

• URL: http://arxiv.org/abs/2510.23009v1
• Date: Mon, 27 Oct 2025 05:01:57 GMT
• Title: UGAE: Unified Geometry and Attribute Enhancement for G-PCC Compressed Point Clouds
• Abstract summary: We propose a unified geometry and attribute enhancement framework.<n>It consists of three core components: post-geometry enhancement (PoGE), pre-attribute enhancement (PAE), and post-attribute enhancement (PoAE)
• Score: 16.087557412139656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lossy compression of point clouds reduces storage and transmission costs; however, it inevitably leads to irreversible distortion in geometry structure and attribute information. To address these issues, we propose a unified geometry and attribute enhancement (UGAE) framework, which consists of three core components: post-geometry enhancement (PoGE), pre-attribute enhancement (PAE), and post-attribute enhancement (PoAE). In PoGE, a Transformer-based sparse convolutional U-Net is used to reconstruct the geometry structure with high precision by predicting voxel occupancy probabilities. Building on the refined geometry structure, PAE introduces an innovative enhanced geometry-guided recoloring strategy, which uses a detail-aware K-Nearest Neighbors (DA-KNN) method to achieve accurate recoloring and effectively preserve high-frequency details before attribute compression. Finally, at the decoder side, PoAE uses an attribute residual prediction network with a weighted mean squared error (W-MSE) loss to enhance the quality of high-frequency regions while maintaining the fidelity of low-frequency regions. UGAE significantly outperformed existing methods on three benchmark datasets: 8iVFB, Owlii, and MVUB. Compared to the latest G-PCC test model (TMC13v29), UGAE achieved an average BD-PSNR gain of 9.98 dB and 90.98% BD-bitrate savings for geometry under the D1 metric, as well as a 3.67 dB BD-PSNR improvement with 56.88% BD-bitrate savings for attributes on the Y component. Additionally, it improved perceptual quality significantly.

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