  Geometry-aware modules are widely applied in recent deep learning
architectures for scene representation and rendering. However, these modules
require intrinsic camera information that might not be obtained accurately. In
this paper, we propose a Spatial Transformation Routing (STR) mechanism to
model the spatial properties without applying any geometric prior. The STR
mechanism treats the spatial transformation as the message passing process, and
the relation between the view poses and the routing weights is modeled by an
end-to-end trainable neural network. Besides, an Occupancy Concept Mapping
(OCM) framework is proposed to provide explainable rationals for scene-fusion
processes. We conducted experiments on several datasets and show that the
proposed STR mechanism improves the performance of the Generative Query Network
(GQN). The visualization results reveal that the routing process can pass the
observed information from one location of some view to the associated location
in the other view, which demonstrates the advantage of the proposed model in
terms of spatial cognition.
