Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

6-2024

Abstract

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.

Keywords

Computer vision, Accuracy, Smoothing methods, Codes, Image color analysis, Navigation, Pattern recognition

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024) : Seattle, WA, USA, June 16-22

First Page

20342

Last Page

20351

Identifier

10.1109/CVPR52733.2024.01923

Publisher

IEEE

City or Country

Seattle, USA

Additional URL

https://doi.org/10.1109/CVPR52733.2024.01923

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