  Most modern deep learning-based multi-view 3D reconstruction techniques use
RNNs or fusion modules to combine information from multiple images after
independently encoding them. These two separate steps have loose connections
and do not allow easy information sharing among views. We propose LegoFormer, a
transformer model for voxel-based 3D reconstruction that uses the attention
layers to share information among views during all computational stages.
Moreover, instead of predicting each voxel independently, we propose to
parametrize the output with a series of low-rank decomposition factors. This
reformulation allows the prediction of an object as a set of independent
regular structures then aggregated to obtain the final reconstruction.
Experiments conducted on ShapeNet demonstrate the competitive performance of
our model with respect to the state of the art while having increased
interpretability thanks to the self-attention layers. We also show promising
generalization results to real data.
