Point cloud enhancement by projection sampling
Abstract 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 introdu...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-03-01
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| Series: | EURASIP Journal on Image and Video Processing |
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| Online Access: | https://doi.org/10.1186/s13640-025-00665-4 |
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| _version_ | 1850072169462104064 |
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| author | Paolo Giannitrapani Tiziana Cattai Stefania Colonnese |
| author_facet | Paolo Giannitrapani Tiziana Cattai Stefania Colonnese |
| author_sort | Paolo Giannitrapani |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-7c5482d7d69d431699fdd845b02c24a6 |
| institution | DOAJ |
| issn | 1687-5281 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Image and Video Processing |
| spelling | doaj-art-7c5482d7d69d431699fdd845b02c24a62025-08-20T02:47:07ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-52812025-03-012025113510.1186/s13640-025-00665-4Point cloud enhancement by projection samplingPaolo Giannitrapani0Tiziana Cattai1Stefania Colonnese2Department of Information Engineering, Electronics and Telecommunications, Sapienza University of RomeDepartment of Information Engineering, Electronics and Telecommunications, Sapienza University of RomeDepartment of Information Engineering, Electronics and Telecommunications, Sapienza University of RomeAbstract 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.https://doi.org/10.1186/s13640-025-00665-4Point cloud (PC)eXtended Reality (XR)Image quality assessmentDiffusion models |
| spellingShingle | Paolo Giannitrapani Tiziana Cattai Stefania Colonnese Point cloud enhancement by projection sampling EURASIP Journal on Image and Video Processing Point cloud (PC) eXtended Reality (XR) Image quality assessment Diffusion models |
| title | Point cloud enhancement by projection sampling |
| title_full | Point cloud enhancement by projection sampling |
| title_fullStr | Point cloud enhancement by projection sampling |
| title_full_unstemmed | Point cloud enhancement by projection sampling |
| title_short | Point cloud enhancement by projection sampling |
| title_sort | point cloud enhancement by projection sampling |
| topic | Point cloud (PC) eXtended Reality (XR) Image quality assessment Diffusion models |
| url | https://doi.org/10.1186/s13640-025-00665-4 |
| work_keys_str_mv | AT paologiannitrapani pointcloudenhancementbyprojectionsampling AT tizianacattai pointcloudenhancementbyprojectionsampling AT stefaniacolonnese pointcloudenhancementbyprojectionsampling |