  Semantic scene completion (SSC) aims to complete a partial 3D scene and
predict its semantics simultaneously. Most existing works adopt the voxel
representations, thus suffering from the growth of memory and computation cost
as the voxel resolution increases. Though a few works attempt to solve SSC from
the perspective of 3D point clouds, they have not fully exploited the
correlation and complementarity between the two tasks of scene completion and
semantic segmentation. In our work, we present CasFusionNet, a novel cascaded
network for point cloud semantic scene completion by dense feature fusion.
Specifically, we design (i) a global completion module (GCM) to produce an
upsampled and completed but coarse point set, (ii) a semantic segmentation
module (SSM) to predict the per-point semantic labels of the completed points
generated by GCM, and (iii) a local refinement module (LRM) to further refine
the coarse completed points and the associated labels from a local perspective.
We organize the above three modules via dense feature fusion in each level, and
cascade a total of four levels, where we also employ feature fusion between
each level for sufficient information usage. Both quantitative and qualitative
results on our compiled two point-based datasets validate the effectiveness and
superiority of our CasFusionNet compared to state-of-the-art methods in terms
of both scene completion and semantic segmentation. The codes and datasets are
available at: https://github.com/JinfengX/CasFusionNet.
