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.

本文提出 PSGformer，一种新颖的 3D 实例分割网络，通过多级语义聚合模块和平行特征融合 Transformer 模块，完整地利用全局和局部语义信息，有效提高了分割水平，实现在 ScanNetv2 数据集上取得了当前最佳结果。

PSGformer：通过精确的语义引导增强 3D 点云实例分割

PSGformer: Enhancing 3D Point Cloud Instance Segmentation via Precise  Semantic Guidance

We propose 3DETR, an end-to-end Transformer based object detection model for
3D point clouds. Compared to existing detection methods that employ a number of
3D-specific inductive biases, 3DETR requires minimal modifications to the
vanilla Transformer block. Specifically, we find that a standard Transformer
with non-parametric queries and Fourier positional embeddings is competitive
with specialized architectures that employ libraries of 3D-specific operators
with hand-tuned hyperparameters. Nevertheless, 3DETR is conceptually simple and
easy to implement, enabling further improvements by incorporating 3D domain
knowledge. Through extensive experiments, we show 3DETR outperforms the
well-established and highly optimized VoteNet baselines on the challenging
ScanNetV2 dataset by 9.5%. Furthermore, we show 3DETR is applicable to 3D tasks
beyond detection, and can serve as a building block for future research.

3DETR 是一种基于 Transformer 的端到端目标检测模型，适用于 3D 点云，相较于现有的检测方法，它需要最少的修改，可以通过将 3D 领域知识整合进去得到进一步的改进，在 ScanNetV2 数据集上，相比于 VoteNet 基线具有 9.5% 更好的性能，并且适用于其他 3D 任务。