Depth-Guided Robust and Fast Point Cloud Fusion NeRF for Sparse Input
  Views
Shuai Guo Qiuwen Wang Yijie Gao Rong Xie Li Song
Novel-view synthesis with sparse input views is important for real-world
applications like AR/VR and autonomous driving. Recent methods have integrated
depth information into NeRFs for sparse input synthesis, leveraging depth prior
for geometric and spatial understanding. However, most existing works tend to
overlook inaccuracies within depth maps and have low time efficiency. To
address these issues, we propose a depth-guided robust and fast point cloud
fusion NeRF for sparse inputs. We perceive radiance fields as an explicit voxel
grid of features. A point cloud is constructed for each input view,
characterized within the voxel grid using matrices and vectors. We accumulate
the point cloud of each input view to construct the fused point cloud of the
entire scene. Each voxel determines its density and appearance by referring to
the point cloud of the entire scene. Through point cloud fusion and voxel grid
fine-tuning, inaccuracies in depth values are refined or substituted by those
from other views. Moreover, our method can achieve faster reconstruction and
greater compactness through effective vector-matrix decomposition. Experimental
results underline the superior performance and time efficiency of our approach
compared to state-of-the-art baselines.