  Current methodologies in point cloud analysis predominantly explore 3D
geometries, often achieved through the introduction of intricate learnable
geometric extractors in the encoder or by deepening networks with repeated
blocks. However, these approaches inevitably lead to a significant number of
learnable parameters, resulting in substantial computational costs and imposing
memory burdens on CPU/GPU. Additionally, the existing strategies are primarily
tailored for object-level point cloud classification and segmentation tasks,
with limited extensions to crucial scene-level applications, such as autonomous
driving. In response to these limitations, we introduce PointeNet, an efficient
network designed specifically for point cloud analysis. PointeNet distinguishes
itself with its lightweight architecture, low training cost, and plug-and-play
capability, effectively capturing representative features. The network consists
of a Multivariate Geometric Encoding (MGE) module and an optional
Distance-aware Semantic Enhancement (DSE) module. The MGE module employs
operations of sampling, grouping, and multivariate geometric aggregation to
lightweightly capture and adaptively aggregate multivariate geometric features,
providing a comprehensive depiction of 3D geometries. The DSE module, designed
for real-world autonomous driving scenarios, enhances the semantic perception
of point clouds, particularly for distant points. Our method demonstrates
flexibility by seamlessly integrating with a classification/segmentation head
or embedding into off-the-shelf 3D object detection networks, achieving notable
performance improvements at a minimal cost. Extensive experiments on
object-level datasets, including ModelNet40, ScanObjectNN, ShapeNetPart, and
the scene-level dataset KITTI, demonstrate the superior performance of
PointeNet over state-of-the-art methods in point cloud analysis.
