3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation

• URL: http://arxiv.org/abs/2109.10161v1
• Date: Tue, 21 Sep 2021 13:16:46 GMT
• Title: 3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation
• Authors: Mengxi Wu, Hao Huang, Yi Fang
• Abstract summary: We show that training with adversarial samples can improve the performance of neural networks on 3D point cloud completion tasks. We propose a novel approach to generate adversarial samples that benefit both the performance of clean and adversarial samples. Experimental results show that training with the adversarial samples crafted by our method effectively enhances the performance of PCN on the ShapeNet dataset.
• Score: 11.198650616143219
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the popularity of 3D sensors in self-driving and other robotics applications, extensive research has focused on designing novel neural network architectures for accurate 3D point cloud completion. However, unlike in point cloud classification and reconstruction, the role of adversarial samples in3D point cloud completion has seldom been explored. In this work, we show that training with adversarial samples can improve the performance of neural networks on 3D point cloud completion tasks. We propose a novel approach to generate adversarial samples that benefit both the performance of clean and adversarial samples. In contrast to the PGD-k attack, our method generates adversarial samples that keep the geometric features in clean samples and contain few outliers. In particular, we use principal directions to constrain the adversarial perturbations for each input point. The gradient components in the mean direction of principal directions are taken as adversarial perturbations. In addition, we also investigate the effect of using the minimum curvature direction. Besides, we adopt attack strength accumulation and auxiliary Batch Normalization layers method to speed up the training process and alleviate the distribution mismatch between clean and adversarial samples. Experimental results show that training with the adversarial samples crafted by our method effectively enhances the performance of PCN on the ShapeNet dataset.

Related papers

• Improving Adversarial Robustness for 3D Point Cloud Recognition at Test-Time through Purified Self-Training [9.072521170921712]
  3D point cloud deep learning model is vulnerable to adversarial attacks. adversarial purification employs generative model to mitigate the impact of adversarial attacks. We propose a test-time purified self-training strategy to achieve this objective.
  arXiv  Detail & Related papers  (2024-09-23T11:46:38Z)
• Transferable 3D Adversarial Shape Completion using Diffusion Models [8.323647730916635]
  3D point cloud feature learning has significantly improved the performance of 3D deep-learning models. Existing attack methods primarily focus on white-box scenarios and struggle to transfer to recently proposed 3D deep-learning models. In this paper, we generate high-quality adversarial point clouds using diffusion models. Our proposed attacks outperform state-of-the-art adversarial attack methods against both black-box models and defenses.
  arXiv  Detail & Related papers  (2024-07-14T04:51:32Z)
• PCLD: Point Cloud Layerwise Diffusion for Adversarial Purification [0.8192907805418583]
  Point clouds are extensively employed in a variety of real-world applications such as robotics, autonomous driving and augmented reality. A typical way to assess a model's robustness is through adversarial attacks. We propose Point Cloud Layerwise Diffusion (PCLD), a layerwise diffusion based 3D point cloud defense strategy.
  arXiv  Detail & Related papers  (2024-03-11T13:13:10Z)
• StarNet: Style-Aware 3D Point Cloud Generation [82.30389817015877]
  StarNet is able to reconstruct and generate high-fidelity and even 3D point clouds using a mapping network. Our framework achieves comparable state-of-the-art performance on various metrics in the point cloud reconstruction and generation tasks.
  arXiv  Detail & Related papers  (2023-03-28T08:21:44Z)
• Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples [21.358496646676087]
  We design a teacher-student structured model for 3D anomaly detection. Specifically, we use feature space alignment, dimension zoom, and max pooling to extract the features of the point cloud. Our method only requires very few normal samples to train the student network.
  arXiv  Detail & Related papers  (2022-10-31T12:29:55Z)
• PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack [73.3371797787823]
  Adversarial contrastive learning (ACL) is considered an effective way to improve the robustness of pre-trained models. We present our robust aware loss function to train self-supervised contrastive learning framework adversarially. We validate our method, PointACL on downstream tasks, including 3D classification and 3D segmentation with multiple datasets.
  arXiv  Detail & Related papers  (2022-09-14T22:58:31Z)
• Imperceptible and Robust Backdoor Attack in 3D Point Cloud [62.992167285646275]
  We propose a novel imperceptible and robust backdoor attack (IRBA) to tackle this challenge. We utilize a nonlinear and local transformation, called weighted local transformation (WLT), to construct poisoned samples with unique transformations. Experiments on three benchmark datasets and four models show that IRBA achieves 80%+ ASR in most cases even with pre-processing techniques.
  arXiv  Detail & Related papers  (2022-08-17T03:53:10Z)
• Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation [79.60988242843437]
  We propose a novel approach that achieves self-supervised and magnification-flexible point clouds upsampling simultaneously. Experimental results demonstrate that our self-supervised learning based scheme achieves competitive or even better performance than supervised learning based state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-18T07:18:25Z)
• Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation [90.2445084743881]
  We present a method for semi-supervised point cloud semantic segmentation to adopt unlabeled point clouds in training to boost the model performance. Inspired by the recent contrastive loss in self-supervised tasks, we propose the guided point contrastive loss to enhance the feature representation and model generalization ability.
  arXiv  Detail & Related papers  (2021-10-15T16:38:54Z)
• Point Discriminative Learning for Unsupervised Representation Learning on 3D Point Clouds [54.31515001741987]
  We propose a point discriminative learning method for unsupervised representation learning on 3D point clouds. We achieve this by imposing a novel point discrimination loss on the middle level and global level point features. Our method learns powerful representations and achieves new state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-08-04T15:11:48Z)
• Minimal Adversarial Examples for Deep Learning on 3D Point Clouds [25.569519066857705]
  In this work, we explore adversarial attacks for point cloud-based neural networks. We propose a unified formulation for adversarial point cloud generation that can generalise two different attack strategies. Our method achieves the state-of-the-art performance with higher than 89% and 90% of attack success rate on synthetic and real-world data respectively.
  arXiv  Detail & Related papers  (2020-08-27T11:50:45Z)

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.