Point clouds offer an attractive source of information to complement images
in neural scene representations, especially when few images are available.
Neural rendering methods based on point clouds do exist, but they do not
perform well when the point cloud quality is low -- e.g., sparse or incomplete,
which is often the case with real-world data. We overcome these problems with a
simple representation that aggregates point clouds at multiple scale levels
with sparse voxel grids at different resolutions. To deal with point cloud
sparsity, we average across multiple scale levels -- but only among those that
are valid, i.e., that have enough neighboring points in proximity to the ray of
a pixel. To help model areas without points, we add a global voxel at the
coarsest scale, thus unifying "classical" and point-based NeRF formulations. We
validate our method on the NeRF Synthetic, ScanNet, and KITTI-360 datasets,
outperforming the state of the art by a significant margin.

使用多尺度稀疏体素网格聚合点云，平均多个尺度以克服点云稀疏性，在全局尺度加入体素表示模拟无点区域，并在 NeRF 合成、ScanNet 和 KITTI-360 数据集上验证方法，显著优于现有技术。