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dc.contributor.authorKopanas, Georgiosen_US
dc.contributor.authorDrettakis, Georgeen_US
dc.contributor.editorGuthe, Michaelen_US
dc.contributor.editorGrosch, Thorstenen_US
dc.date.accessioned2023-09-25T11:36:23Z
dc.date.available2023-09-25T11:36:23Z
dc.date.issued2023
dc.identifier.isbn978-3-03868-232-5
dc.identifier.urihttps://doi.org/10.2312/vmv.20231222
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20231222
dc.description.abstractNeural Radiance Fields, or NeRFs, have drastically improved novel view synthesis and 3D reconstruction for rendering. NeRFs achieve impressive results on object-centric reconstructions, but the quality of novel view synthesis with free-viewpoint navigation in complex environments (rooms, houses, etc) is often problematic. While algorithmic improvements play an important role in the resulting quality of novel view synthesis, in this work, we show that because optimizing a NeRF is inherently a data-driven process, good quality data play a fundamental role in the final quality of the reconstruction. As a consequence, it is critical to choose the data samples - in this case the cameras - in a way that will eventually allow the optimization to converge to a solution that allows free-viewpoint navigation with good quality. Our main contribution is an algorithm that efficiently proposes new camera placements that improve visual quality with minimal assumptions. Our solution can be used with any NeRF model and outperforms baselines and similar work.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCCS Concepts: Computing methodologies → Computer graphics; Rendering; Active learning settings
dc.subjectComputing methodologies → Computer graphics
dc.subjectRendering
dc.subjectActive learning settings
dc.titleImproving NeRF Quality by Progressive Camera Placement for Free-Viewpoint Navigationen_US
dc.description.seriesinformationVision, Modeling, and Visualization
dc.description.sectionheadersRendering and Modelling
dc.identifier.doi10.2312/vmv.20231222
dc.identifier.pages11-20
dc.identifier.pages10 pages


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