In many practical fluid dynamics experiments, measuring variables such as
velocity and pressure is possible only at a limited number of sensor locations,
for a few two-dimensional planes, or for a small 3D domain in the flow. However, knowledge of the full fields is necessary to understand the dynamics
of many flows. Deep learning reconstruction of full flow fields from sparse
measurements has recently garnered significant research interest, as a way of
overcoming this limitation. This task is referred to as the flow reconstruction (FR) task. In the present study, we propose a convolutional autoencoder
based neural network model, dubbed FR3D, which enables FR to be carried
out for three-dimensional flows around extruded 3D objects with different
cross-sections. An innovative mapping approach, whereby multiple fluid domains are mapped to an annulus, enables FR3D to generalize its performance
to objects not encountered during training. We conclusively demonstrate
this generalization capability using a dataset composed of 80 training and 20
testing geometries, all randomly generated. We show that the FR3D model
reconstructs pressure and velocity components with a few percentage points
of error. Additionally, using these predictions, we accurately estimate the
Q-criterion fields as well lift and drag forces on the geometries.