  The interaction and dimension of points are two important axes in designing
point operators to serve hierarchical 3D models. Yet, these two axes are
heterogeneous and challenging to fully explore. Existing works craft point
operator under a single axis and reuse the crafted operator in all parts of 3D
models. This overlooks the opportunity to better combine point interactions and
dimensions by exploiting varying geometry/density of 3D point clouds. In this
work, we establish PIDS, a novel paradigm to jointly explore point interactions
and point dimensions to serve semantic segmentation on point cloud data. We
establish a large search space to jointly consider versatile point interactions
and point dimensions. This supports point operators with various
geometry/density considerations. The enlarged search space with heterogeneous
search components calls for a better ranking of candidate models. To achieve
this, we improve the search space exploration by leveraging predictor-based
Neural Architecture Search (NAS), and enhance the quality of prediction by
assigning unique encoding to heterogeneous search components based on their
priors. We thoroughly evaluate the networks crafted by PIDS on two semantic
segmentation benchmarks, showing ~1% mIOU improvement on SemanticKITTI and
S3DIS over state-of-the-art 3D models.
