  Semantic segmentation of point clouds, aiming to assign each point a semantic
category, is critical to 3D scene understanding.Despite of significant advances
in recent years, most of existing methods still suffer from either the
object-level misclassification or the boundary-level ambiguity. In this paper,
we present a robust semantic segmentation network by deeply exploring the
geometry of point clouds, dubbed GeoSegNet. Our GeoSegNet consists of a
multi-geometry based encoder and a boundary-guided decoder. In the encoder, we
develop a new residual geometry module from multi-geometry perspectives to
extract object-level features. In the decoder, we introduce a contrastive
boundary learning module to enhance the geometric representation of boundary
points. Benefiting from the geometric encoder-decoder modeling, our GeoSegNet
can infer the segmentation of objects effectively while making the
intersections (boundaries) of two or more objects clear. Experiments show
obvious improvements of our method over its competitors in terms of the overall
segmentation accuracy and object boundary clearness. Code is available at
https://github.com/Chen-yuiyui/GeoSegNet.
