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<p>Next-generation networks are designed to support novel Extended Reality services, which often use point clouds (PCs) to represent real 3D objects. Enhancing the visual quality of PCs by correcting acquisition or transmission errors can significantly improve such services. Herein, we introduce a novel PC enhancement method acting on the PC bidimensional projections computed within the MPEG Video Point Cloud Coding algorithm. We first show that these PC projections are statistically similar to natural images and then we apply to them advanced training-free diffusion sampling. The enhanced projections are then mapped back into the 3D domain, resulting in PC enhancement, which we refer to as Projection Sampling based Point Cloud Enhancement (PS-PCE). We validate PS-PCE on publicly available PCs under various distortions and we compare it with state-of-the-art alternatives. Our results show that the proposed method effectively enhances the PCs, opening new possibilities for PC enhancement in extended reality services.</p>

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Point cloud enhancement by projection sampling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月7日

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  • Paolo Giannitrapani,
  • Tiziana Cattai,
  • Stefania Colonnese

摘要

Next-generation networks are designed to support novel Extended Reality services, which often use point clouds (PCs) to represent real 3D objects. Enhancing the visual quality of PCs by correcting acquisition or transmission errors can significantly improve such services. Herein, we introduce a novel PC enhancement method acting on the PC bidimensional projections computed within the MPEG Video Point Cloud Coding algorithm. We first show that these PC projections are statistically similar to natural images and then we apply to them advanced training-free diffusion sampling. The enhanced projections are then mapped back into the 3D domain, resulting in PC enhancement, which we refer to as Projection Sampling based Point Cloud Enhancement (PS-PCE). We validate PS-PCE on publicly available PCs under various distortions and we compare it with state-of-the-art alternatives. Our results show that the proposed method effectively enhances the PCs, opening new possibilities for PC enhancement in extended reality services.