PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies

• URL: http://arxiv.org/abs/2206.04670v1
• Date: Thu, 9 Jun 2022 17:59:54 GMT
• Title: PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
• Authors: Guocheng Qian, Yuchen Li, Houwen Peng, Jinjie Mai, Hasan Abed Al Kader Hammoud, Mohamed Elhoseiny, Bernard Ghanem
• Abstract summary: We revisit the classical PointNet++ through a systematic study of model training and scaling strategies. We propose a set of improved training strategies that significantly improve PointNet++ performance. We introduce an inverted residual bottleneck design and separables into PointNet++ to enable efficient and effective model scaling.
• Score: 85.14697849950392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: PointNet++ is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet++ has been largely surpassed by recent networks such as PointMLP and Point Transformer, we find that a large portion of the performance gain is due to improved training strategies, i.e. data augmentation and optimization techniques, and increased model sizes rather than architectural innovations. Thus, the full potential of PointNet++ has yet to be explored. In this work, we revisit the classical PointNet++ through a systematic study of model training and scaling strategies, and offer two major contributions. First, we propose a set of improved training strategies that significantly improve PointNet++ performance. For example, we show that, without any change in architecture, the overall accuracy (OA) of PointNet++ on ScanObjectNN object classification can be raised from 77.9\% to 86.1\%, even outperforming state-of-the-art PointMLP. Second, we introduce an inverted residual bottleneck design and separable MLPs into PointNet++ to enable efficient and effective model scaling and propose PointNeXt, the next version of PointNets. PointNeXt can be flexibly scaled up and outperforms state-of-the-art methods on both 3D classification and segmentation tasks. For classification, PointNeXt reaches an overall accuracy of $87.7\%$ on ScanObjectNN, surpassing PointMLP by $2.3\%$, while being $10 \times$ faster in inference. For semantic segmentation, PointNeXt establishes a new state-of-the-art performance with $74.9\%$ mean IoU on S3DIS (6-fold cross-validation), being superior to the recent Point Transformer. The code and models are available at https://github.com/guochengqian/pointnext.

Related papers

• PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point Sets [1.90365714903665]
  We introduce PointNet-KAN, a neural network for 3D point cloud classification and segmentation tasks. It employs Kolmogorov-Arnold Networks (KANs) instead of traditional Multilayer Perceptrons (MLPs)
  arXiv  Detail & Related papers  (2024-10-14T01:57:06Z)
• Local Neighborhood Features for 3D Classification [7.081193814489042]
  We revisit the PointNeXt model to study the usage and benefit of such neighborhood point features. We gain 0.5%, 1%, 4.8%, 3.4%, and 1.6% overall accuracy on the PointNeXt model with real-world datasets.
  arXiv  Detail & Related papers  (2022-12-09T22:53:40Z)
• Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework [55.40001810884942]
  We introduce a pure residual network, called PointMLP, which integrates no sophisticated local geometrical extractors but still performs very competitively. On the real-world ScanObjectNN dataset, our method even surpasses the prior best method by 3.3% accuracy. Compared to most recent CurveNet, PointMLP trains 2x faster, tests 7x faster, and is more accurate on ModelNet40 benchmark.
  arXiv  Detail & Related papers  (2022-02-15T01:39:07Z)
• GSIP: Green Semantic Segmentation of Large-Scale Indoor Point Clouds [64.86292006892093]
  GSIP (Green of Indoor Point clouds) is an efficient solution to semantic segmentation of large-scale indoor scene point clouds. GSIP has two novel components: 1) a room-style data pre-processing method that selects a proper subset of points for further processing, and 2) a new feature extractor which is extended from PointHop. Experiments show that GSIP outperforms PointNet in segmentation performance for the S3DIS dataset.
  arXiv  Detail & Related papers  (2021-09-24T09:26:53Z)
• Learning point embedding for 3D data processing [2.12121796606941]
  Current point-based methods are essentially spatial relationship processing networks. Our architecture, PE-Net, learns the representation of point clouds in high-dimensional space. Experiments show that PE-Net achieves the state-of-the-art performance in multiple challenging datasets.
  arXiv  Detail & Related papers  (2021-07-19T00:25:28Z)
• Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline [111.3236030935478]
  We find that auxiliary factors like different evaluation schemes, data augmentation strategies, and loss functions make a large difference in performance. A projection-based method, which we refer to as SimpleView, performs surprisingly well. It achieves on par or better results than sophisticated state-of-the-art methods on ModelNet40 while being half the size of PointNet++.
  arXiv  Detail & Related papers  (2021-06-09T18:01:11Z)
• IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration [68.88711148515682]
  Deep neural networks are vulnerable to various 3D adversarial attacks. We propose an IF-Defense framework to directly optimize the coordinates of input points with geometry-aware and distribution-aware constraints. Our results show that IF-Defense achieves the state-of-the-art defense performance against existing 3D adversarial attacks on PointNet, PointNet++, DGCNN, PointConv and RS-CNN.
  arXiv  Detail & Related papers  (2020-10-11T15:36:40Z)
• PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification [55.887502438160304]
  The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction. We improve the PointHop method furthermore in two aspects: 1) reducing its model complexity in terms of the model parameter number and 2) ordering discriminant features automatically based on the cross-entropy criterion. With experiments conducted on the ModelNet40 benchmark dataset, we show that the PointHop++ method performs on par with deep neural network (DNN) solutions and surpasses other unsupervised feature extraction methods.
  arXiv  Detail & Related papers  (2020-02-09T04:49:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.