Existing 3D semantic segmentation methods rely on point-wise or voxel-wise
feature descriptors to output segmentation predictions. However, these
descriptors are often supervised at point or voxel level, leading to
segmentation models that can behave poorly at instance-level. In this paper, we
proposed a novel instance-aware approach for 3D semantic segmentation. Our
method combines several geometry processing tasks supervised at instance-level
to promote the consistency of the learned feature representation. Specifically,
our methods use shape generators and shape classifiers to perform shape
reconstruction and classification tasks for each shape instance. This enforces
the feature representation to faithfully encode both structural and local shape
information, with an awareness of shape instances. In the experiments, our
method significantly outperform existing approaches in 3D semantic segmentation
on several public benchmarks, such as Waymo Open Dataset, SemanticKITTI and
ScanNetV2.

本文提出了一种新颖的面向实例的方法，用于 3D 语义分割，通过结合几个在实例级别监督的几何处理任务来促进学习到的特征表示的一致性，具体方法使用形状生成器和形状分类器来执行每个形状实例的形状重建和分类任务，从而使特征表示能够忠实地编码结构和局部形状信息，并意识到形状实例。在实验中，我们的方法在多个公共基准数据集（如 Waymo Open Dataset、SemanticKITTI 和 ScanNetV2）上明显优于现有的 3D 语义分割方法。