Learning with unreliability : Fast few-shot voxel radiance fields with relative geometric consistency

• Main Authors: XU, Yingjie, LIU, Bangzhen, TANG, Hao, DENG, Bailin, HE, Shengfeng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Computer vision; Accuracy; Smoothing methods; Codes; Image color analysis; Navigation; Pattern recognition; Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces
• 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
• Institution: Singapore Management University

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...