Learning with unreliability : Fast few-shot voxel radiance fields with relative geometric consistency
We propose a voxel-based optimization framework, Re VoRF, for few-shot radiance fields that strategically ad-dress the unreliability in pseudo novel view synthesis. Our method pivots on the insight that relative depth relationships within neighboring regions are more reliable than the ab-solute colo...
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Main Authors: | , , , , |
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Format: | text |
Language: | English |
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Institutional Knowledge at Singapore Management University
2024
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Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/9775 https://ink.library.smu.edu.sg/context/sis_research/article/10775/viewcontent/2403.17638v1.pdf |
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Institution: | Singapore Management University |
Language: | English |
Summary: | We propose a voxel-based optimization framework, Re VoRF, for few-shot radiance fields that strategically ad-dress the unreliability in pseudo novel view synthesis. Our method pivots on the insight that relative depth relationships within neighboring regions are more reliable than the ab-solute color values in disoccluded areas. Consequently, we devise a bilateral geometric consistency loss that carefully navigates the trade-off between color fidelity and geometric accuracy in the context of depth consistency for uncertain regions. Moreover, we present a reliability-guided learning strategy to discern and utilize the variable quality across syn-thesized views, complemented by a reliability-aware voxel smoothing algorithm that smoothens the transition between reliable and unreliable data patches. Our approach allows for a more nuanced use of all available data, promoting en-hanced learning from regions previously considered unsuit-able for high-quality reconstruction. Extensive experiments across diverse datasets reveal that our approach attains significant gains in efficiency and accuracy, delivering ren-dering speeds of 3 FPS, 7 mins to train a 360° scene, and a 5% improvement in PSNR over existing few-shot methods. |
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