Learning with Unreliability: Fast Few-shot Voxel Radiance Fields with
  Relative Geometric Consistency
Yingjie Xu Bangzhen Liu Hao Tang Bailin Deng Shengfeng He
We propose a voxel-based optimization framework, ReVoRF, for few-shot
radiance fields that strategically address 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 absolute
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 synthesized 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 enhanced
learning from regions previously considered unsuitable for high-quality
reconstruction. Extensive experiments across diverse datasets reveal that our
approach attains significant gains in efficiency and accuracy, delivering
rendering speeds of 3 FPS, 7 mins to train a $360^\circ$ scene, and a 5\%
improvement in PSNR over existing few-shot methods. Code is available at
https://github.com/HKCLynn/ReVoRF.