  We introduce a new approach for reconstruction and novel-view synthesis of
unbounded real-world scenes. In contrast to previous methods using either
volumetric fields, grid-based models, or discrete point cloud proxies, we
propose a hybrid scene representation, which implicitly encodes a point cloud
in a continuous octree-based probability field and a multi-resolution hash
grid. In doing so, we combine the benefits of both worlds by retaining
favorable behavior during optimization: Our novel implicit point cloud
representation and differentiable bilinear rasterizer enable fast rendering
while preserving fine geometric detail without depending on initial priors like
structure-from-motion point clouds. Our method achieves state-of-the-art image
quality on several common benchmark datasets. Furthermore, we achieve fast
inference at interactive frame rates, and can extract explicit point clouds to
further enhance performance.
