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|a 10.1109/TPAMI.2023.3343395
|2 doi
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|a eng
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| 100 |
1 |
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|a Zhou, Kun
|e verfasserin
|4 aut
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| 245 |
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|a From NeRFLiX to NeRFLiX++
|b A General NeRF-Agnostic Restorer Paradigm
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|c 2024
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 03.04.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Neural radiance fields (NeRF) have shown great success in novel view synthesis. However, recovering high-quality details from real-world scenes is still challenging for the existing NeRF-based approaches, due to the potential imperfect calibration information and scene representation inaccuracy. Even with high-quality training frames, the synthetic novel views produced by NeRF models still suffer from notable rendering artifacts, such as noise and blur. To address this, we propose NeRFLiX, a general NeRF-agnostic restorer paradigm that learns a degradation-driven inter-viewpoint mixer. Specially, we design a NeRF-style degradation modeling approach and construct large-scale training data, enabling the possibility of effectively removing NeRF-native rendering artifacts for deep neural networks. Moreover, beyond the degradation removal, we propose an inter-viewpoint aggregation framework that fuses highly related high-quality training images, pushing the performance of cutting-edge NeRF models to entirely new levels and producing highly photo-realistic synthetic views. Based on this paradigm, we further present NeRFLiX++ with a stronger two-stage NeRF degradation simulator and a faster inter-viewpoint mixer, achieving superior performance with significantly improved computational efficiency. Notably, NeRFLiX++ is capable of restoring photo-realistic ultra-high-resolution outputs from noisy low-resolution NeRF-rendered views. Extensive experiments demonstrate the excellent restoration ability of NeRFLiX++ on various novel view synthesis benchmarks
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|a Journal Article
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| 700 |
1 |
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|a Li, Wenbo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jiang, Nianjuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Han, Xiaoguang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lu, Jiangbo
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 46(2024), 5 vom: 02. Apr., Seite 3422-3437
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
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|g volume:46
|g year:2024
|g number:5
|g day:02
|g month:04
|g pages:3422-3437
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2023.3343395
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