  Semantic scene completion (SSC) requires an accurate understanding of the
geometric and semantic relationships between the objects in the 3D scene for
reasoning the occluded objects. The popular SSC methods voxelize the 3D
objects, allowing the deep 3D convolutional network (3D CNN) to learn the
object relationships from the complex scenes. However, the current networks
lack the controllable kernels to model the object relationship across multiple
views, where appropriate views provide the relevant information for suggesting
the existence of the occluded objects. In this paper, we propose Cross-View
Synthesis Transformer (CVSformer), which consists of Multi-View Feature
Synthesis and Cross-View Transformer for learning cross-view object
relationships. In the multi-view feature synthesis, we use a set of 3D
convolutional kernels rotated differently to compute the multi-view features
for each voxel. In the cross-view transformer, we employ the cross-view fusion
to comprehensively learn the cross-view relationships, which form useful
information for enhancing the features of individual views. We use the enhanced
features to predict the geometric occupancies and semantic labels of all
voxels. We evaluate CVSformer on public datasets, where CVSformer yields
state-of-the-art results.
