PSGformer: Enhancing 3D Point Cloud Instance Segmentation via Precise
  Semantic Guidance
Lei Pan Wuyang Luan Yuan Zheng Qiang Fu Junhui Li
Most existing 3D instance segmentation methods are derived from 3D semantic
segmentation models. However, these indirect approaches suffer from certain
limitations. They fail to fully leverage global and local semantic information
for accurate prediction, which hampers the overall performance of the 3D
instance segmentation framework. To address these issues, this paper presents
PSGformer, a novel 3D instance segmentation network. PSGformer incorporates two
key advancements to enhance the performance of 3D instance segmentation.
Firstly, we propose a Multi-Level Semantic Aggregation Module, which
effectively captures scene features by employing foreground point filtering and
multi-radius aggregation. This module enables the acquisition of more detailed
semantic information from global and local perspectives. Secondly, PSGformer
introduces a Parallel Feature Fusion Transformer Module that independently
processes super-point features and aggregated features using transformers. The
model achieves a more comprehensive feature representation by the features
which connect global and local features. We conducted extensive experiments on
the ScanNetv2 dataset. Notably, PSGformer exceeds compared state-of-the-art
methods by 2.2% on ScanNetv2 hidden test set in terms of mAP. Our code and
models will be publicly released.