  Semantic scene completion is the task of jointly estimating 3D geometry and
semantics of objects and surfaces within a given extent. This is a particularly
challenging task on real-world data that is sparse and occluded. We propose a
scene segmentation network based on local Deep Implicit Functions as a novel
learning-based method for scene completion. Unlike previous work on scene
completion, our method produces a continuous scene representation that is not
based on voxelization. We encode raw point clouds into a latent space locally
and at multiple spatial resolutions. A global scene completion function is
subsequently assembled from the localized function patches. We show that this
continuous representation is suitable to encode geometric and semantic
properties of extensive outdoor scenes without the need for spatial
discretization (thus avoiding the trade-off between level of scene detail and
the scene extent that can be covered).
  We train and evaluate our method on semantically annotated LiDAR scans from
the Semantic KITTI dataset. Our experiments verify that our method generates a
powerful representation that can be decoded into a dense 3D description of a
given scene. The performance of our method surpasses the state of the art on
the Semantic KITTI Scene Completion Benchmark in terms of geometric completion
intersection-over-union (IoU).
