Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification

• URL: http://arxiv.org/abs/2307.10875v3
• Date: Tue, 2 Jan 2024 04:15:02 GMT
• Title: Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification
• Authors: Xinke Li, Junchi Lu, Henghui Ding, Changsheng Sun, Joey Tianyi Zhou, Chee Yeow Meng
• Abstract summary: This paper highlights the challenges of point cloud classification posed by various forms of noise. We introduce an innovative point outlier cleansing method that harnesses the power of downstream classification models. Our proposed technique not only robustly filters diverse point cloud outliers but also consistently and significantly enhances existing robust methods for point cloud classification.
• Score: 54.286437930350445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the growth of 3D sensing technology, deep learning system for 3D point clouds has become increasingly important, especially in applications like autonomous vehicles where safety is a primary concern. However, there are also growing concerns about the reliability of these systems when they encounter noisy point clouds, whether occurring naturally or introduced with malicious intent. This paper highlights the challenges of point cloud classification posed by various forms of noise, from simple background noise to malicious backdoor attacks that can intentionally skew model predictions. While there's an urgent need for optimized point cloud denoising, current point outlier removal approaches, an essential step for denoising, rely heavily on handcrafted strategies and are not adapted for higher-level tasks, such as classification. To address this issue, we introduce an innovative point outlier cleansing method that harnesses the power of downstream classification models. By employing gradient-based attribution analysis, we define a novel concept: point risk. Drawing inspiration from tail risk minimization in finance, we recast the outlier removal process as an optimization problem, named PointCVaR. Extensive experiments show that our proposed technique not only robustly filters diverse point cloud outliers but also consistently and significantly enhances existing robust methods for point cloud classification.

Related papers

• iBA: Backdoor Attack on 3D Point Cloud via Reconstructing Itself [5.007492246056274]
  MirrorAttack is a novel effective 3D backdoor attack method. It implants the trigger by simply reconstructing a clean point cloud with an auto-encoder. We achieve state-of-the-art ASR on different types of victim models with the intervention of defensive techniques.
  arXiv  Detail & Related papers  (2024-03-09T09:15:37Z)
• Point Cloud Pre-training with Diffusion Models [62.12279263217138]
  We propose a novel pre-training method called Point cloud Diffusion pre-training (PointDif) PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification, segmentation and detection.
  arXiv  Detail & Related papers  (2023-11-25T08:10:05Z)
• 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)
• Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion [70.60038549155485]
  Deep 3D point cloud models are sensitive to adversarial attacks, which poses threats to safety-critical applications such as autonomous driving. This paper introduces a novel distortion-aware defense framework that can rebuild the pristine data distribution with a tailored intensity estimator and a diffusion model.
  arXiv  Detail & Related papers  (2022-11-29T14:32:43Z)
• Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding [61.30276576646909]
  We propose an augmentation-free unsupervised approach for point clouds to learn transferable point-level features via soft clustering, named SoftClu. We exploit the affiliation of points to their clusters as a proxy to enable self-training through a pseudo-label prediction task.
  arXiv  Detail & Related papers  (2022-10-06T10:18:16Z)
• PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition [111.55944556661626]
  We propose Point-Cloud Contrastive Adversarial Training (PointCAT) to boost the robustness of point cloud recognition models. We leverage a supervised contrastive loss to facilitate the alignment and uniformity of the hypersphere features extracted by the recognition model. To provide the more challenging corrupted point clouds, we adversarially train a noise generator along with the recognition model from the scratch.
  arXiv  Detail & Related papers  (2022-09-16T08:33:04Z)
• Adversarial Attack by Limited Point Cloud Surface Modifications [11.325135016306165]
  adversarial attack methods do not restrict the point modifications enough to preserve the point cloud appearance. The proposed method addresses this issue by using a high step-size at the beginning of the algorithm to search the main surface of the point cloud. The obtained results show that it can perform successful attacks and achieve state-of-the-art results by only a limited number of point modifications.
  arXiv  Detail & Related papers  (2021-10-07T18:58:18Z)

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.