  Current point cloud segmentation architectures suffer from limited long-range
feature modeling, as they mostly rely on aggregating information with local
neighborhoods. Furthermore, in order to learn point features at multiple
scales, most methods utilize a data-agnostic sampling approach to decrease the
number of points after each stage. Such sampling methods, however, often
discard points for small objects in the early stages, leading to inadequate
feature learning. We believe these issues are can be mitigated by introducing
explicit geometry clues as guidance. To this end, we propose GeoSpark, a
Plug-in module that incorporates Geometry clues into the network to Spark up
feature learning and downsampling. GeoSpark can be easily integrated into
various backbones. For feature aggregation, it improves feature modeling by
allowing the network to learn from both local points and neighboring geometry
partitions, resulting in an enlarged data-tailored receptive field.
Additionally, GeoSpark utilizes geometry partition information to guide the
downsampling process, where points with unique features are preserved while
redundant points are fused, resulting in better preservation of key points
throughout the network. We observed consistent improvements after adding
GeoSpark to various backbones including PointNet++, KPConv, and
PointTransformer. Notably, when integrated with Point Transformer, our GeoSpark
module achieves a 74.7% mIoU on the ScanNetv2 dataset (4.1% improvement) and
71.5% mIoU on the S3DIS Area 5 dataset (1.1% improvement), ranking top on both
benchmarks. Code and models will be made publicly available.
