  In this work, a language-level Semantics Conditioned framework for 3D Point
cloud segmentation, called SeCondPoint, is proposed, where language-level
semantics are introduced to condition the modeling of point feature
distribution as well as the pseudo-feature generation, and a
feature-geometry-based mixup approach is further proposed to facilitate the
distribution learning. To our knowledge, this is the first attempt in
literature to introduce language-level semantics to the 3D point cloud
segmentation task. Since a large number of point features could be generated
from the learned distribution thanks to the semantics conditioned modeling, any
existing segmentation network could be embedded into the proposed framework to
boost its performance. In addition, the proposed framework has the inherent
advantage of dealing with novel classes, which seems an impossible feat for the
current segmentation networks. Extensive experimental results on two public
datasets demonstrate that three typical segmentation networks could achieve
significant improvements over their original performances after enhancement by
the proposed framework in the conventional 3D segmentation task. Two benchmarks
are also introduced for a newly introduced zero-shot 3D segmentation task, and
the results also validate the proposed framework.
