  Downsampling and feature extraction are essential procedures for 3D point
cloud understanding. Existing methods are limited by the inconsistent point
densities of different parts in the point cloud. In this work, we analyze the
limitation of the downsampling stage and propose the pre-abstraction group-wise
window-normalization module. In particular, the window-normalization method is
leveraged to unify the point densities in different parts. Furthermore, the
group-wise strategy is proposed to obtain multi-type features, including
texture and spatial information. We also propose the pre-abstraction module to
balance local and global features. Extensive experiments show that our module
performs better on several tasks. In segmentation tasks on S3DIS (Area 5), the
proposed module performs better on small object recognition, and the results
have more precise boundaries than others. The recognition of the sofa and the
column is improved from 69.2% to 84.4% and from 42.7% to 48.7%, respectively.
The benchmarks are improved from 71.7%/77.6%/91.9% (mIoU/mAcc/OA) to
72.2%/78.2%/91.4%. The accuracies of 6-fold cross-validation on S3DIS are
77.6%/85.8%/91.7%. It outperforms the best model PointNeXt-XL
(74.9%/83.0%/90.3%) by 2.7% on mIoU and achieves state-of-the-art performance.
The code and models are available at
https://github.com/DBDXSS/Window-Normalization.git.
