  In this work, we focus on designing a point local aggregation function that
yields parameter efficient networks for 3D point cloud semantic segmentation.
We explore the idea of using learnable neighbor-to-grid soft assignment in
grid-based aggregation functions. Previous methods in literature operate on a
predefined geometric grid such as local volume partitions or irregular kernel
points. A more general alternative is to allow the network to learn an
assignment function that best suits the end task. Since it is learnable, this
mapping is allowed to be different per layer instead of being applied uniformly
throughout the depth of the network. By endowing the network with the
flexibility to learn its own neighbor-to-grid assignment, we arrive at
parameter efficient models that achieve state-of-the-art (SOTA) performance on
S3DIS with at least 10$\times$ less parameters than the current reigning
method. We also demonstrate competitive performance on ScanNet and PartNet
compared with much larger SOTA models.
