  While current state-of-the-art generalizable implicit neural shape models
rely on the inductive bias of convolutions, it is still not entirely clear how
properties emerging from such biases are compatible with the task of 3D
reconstruction from point cloud. We explore an alternative approach to
generalizability in this context. We relax the intrinsic model bias (i.e. using
MLPs to encode local features as opposed to convolutions) and constrain the
hypothesis space instead with an auxiliary regularization related to the
reconstruction task, i.e. denoising. The resulting model is the first only-MLP
locally conditioned implicit shape reconstruction from point cloud network with
fast feed forward inference. Point cloud borne features and denoising offsets
are predicted from an exclusively MLP-made network in a single forward pass. A
decoder predicts occupancy probabilities for queries anywhere in space by
pooling nearby features from the point cloud order-invariantly, guided by
denoised relative positional encoding. We outperform the state-of-the-art
convolutional method while using half the number of model parameters.
