Neural Radiance Fields (NeRF) has shown great success in novel view synthesis
due to its state-of-the-art quality and flexibility. However, NeRF requires
dense input views (tens to hundreds) and a long training time (hours to days)
for a single scene to generate high-fidelity images. Although using the voxel
grids to represent the radiance field can significantly accelerate the
optimization process, we observe that for sparse inputs, the voxel grids are
more prone to overfitting to the training views and will have holes and
floaters, which leads to artifacts. In this paper, we propose VGOS, an approach
for fast (3-5 minutes) radiance field reconstruction from sparse inputs (3-10
views) to address these issues. To improve the performance of voxel-based
radiance field in sparse input scenarios, we propose two methods: (a) We
introduce an incremental voxel training strategy, which prevents overfitting by
suppressing the optimization of peripheral voxels in the early stage of
reconstruction. (b) We use several regularization techniques to smooth the
voxels, which avoids degenerate solutions. Experiments demonstrate that VGOS
achieves state-of-the-art performance for sparse inputs with super-fast
convergence. Code will be available at this https URL

本研究提出了一种名为 VGOS 的方法，通过 voxel-based radiance field 技术来快速重建稀疏输入（3-10 视角）下的辐射场，该方法采用了增量训练策略和正则化技术，以达到防止过拟合和平滑的效果，实验证明 VGOS 在超快速收敛下达到了稀疏输入情景下的最佳表现。