HPRO: Direct Visibility of Point Clouds for Optimization

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dc.contributor.authorKatz, Sagien_US
dc.contributor.authorTal, Ayelleten_US
dc.contributor.editorBousseau, Adrienen_US
dc.contributor.editorDay, Angelaen_US
dc.date.accessioned2025-05-09T09:13:18Z
dc.date.available2025-05-09T09:13:18Z
dc.date.issued2025
dc.description.abstractGiven a point cloud, which is assumed to be a sampling of a continuous surface, and a viewpoint, which points are visible from that viewpoint? Since points do not occlude each other, the real question is which points would be visible if the surface they were sampled from were known. While an existing approximation method addresses this problem, it is unsuitable for use in optimization processes or learning models due to its lack of differentiability. To overcome this limitation, the paper introduces a novel differentiable approximation method. It is based on identifying the extreme points of a point set in a differentiable manner. This approach can be effectively integrated into optimization algorithms or used as a layer in neural networks, allowing for the computation and utilization of visible points in various tasks, such as optimal viewpoint selection. The paper also provides theoretical proofs of the operator's correctness in the limit, further validating its effectiveness. The code is available at https://github.com/sagikatz/HPROen_US
dc.description.number2
dc.description.sectionheadersThe Shape of Rendering
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70046
dc.identifier.issn1467-8659
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70046
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70046
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleHPRO: Direct Visibility of Point Clouds for Optimizationen_US
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