SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency
  Guidance
Yuru Xiao Xianming Liu Deming Zhai Kui Jiang Junjun Jiang Xiangyang Ji
Neural Radiance Field (NeRF) technology has made significant strides in
creating novel viewpoints. However, its effectiveness is hampered when working
with sparsely available views, often leading to performance dips due to
overfitting. FreeNeRF attempts to overcome this limitation by integrating
implicit geometry regularization, which incrementally improves both geometry
and textures. Nonetheless, an initial low positional encoding bandwidth results
in the exclusion of high-frequency elements. The quest for a holistic approach
that simultaneously addresses overfitting and the preservation of
high-frequency details remains ongoing. This study introduces a novel feature
matching based sparse geometry regularization module. This module excels in
pinpointing high-frequency keypoints, thereby safeguarding the integrity of
fine details. Through progressive refinement of geometry and textures across
NeRF iterations, we unveil an effective few-shot neural rendering architecture,
designated as SGCNeRF, for enhanced novel view synthesis. Our experiments
demonstrate that SGCNeRF not only achieves superior geometry-consistent
outcomes but also surpasses FreeNeRF, with improvements of 0.7 dB and 0.6 dB in
PSNR on the LLFF and DTU datasets, respectively.