Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via
3D Anomaly Synthesis and A Self-Supervised Learning Network
- URL: http://arxiv.org/abs/2311.14897v3
- Date: Thu, 30 Nov 2023 04:13:59 GMT
- Title: Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via
3D Anomaly Synthesis and A Self-Supervised Learning Network
- Authors: Wenqiao Li, Xiaohao Xu, Yao Gu, Bozhong Zheng, Shenghua Gao, Yingna Wu
- Abstract summary: We propose a 3D anomaly synthesis pipeline to adapt existing large-scale 3Dmodels for 3D anomaly detection.
Anomaly-ShapeNet consists of 1600 point cloud samples under 40 categories, which provides a rich and varied collection of data.
We also propose a self-supervised method, i.e., Iterative Mask Reconstruction Network (IMRNet), to enable scalable representation learning for 3D anomaly localization.
- Score: 22.81108868492533
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, 3D anomaly detection, a crucial problem involving fine-grained
geometry discrimination, is getting more attention. However, the lack of
abundant real 3D anomaly data limits the scalability of current models. To
enable scalable anomaly data collection, we propose a 3D anomaly synthesis
pipeline to adapt existing large-scale 3Dmodels for 3D anomaly detection.
Specifically, we construct a synthetic dataset, i.e., Anomaly-ShapeNet, basedon
ShapeNet. Anomaly-ShapeNet consists of 1600 point cloud samples under 40
categories, which provides a rich and varied collection of data, enabling
efficient training and enhancing adaptability to industrial scenarios.
Meanwhile,to enable scalable representation learning for 3D anomaly
localization, we propose a self-supervised method, i.e., Iterative Mask
Reconstruction Network (IMRNet). During training, we propose a geometry-aware
sample module to preserve potentially anomalous local regions during point
cloud down-sampling. Then, we randomly mask out point patches and sent the
visible patches to a transformer for reconstruction-based self-supervision.
During testing, the point cloud repeatedly goes through the Mask Reconstruction
Network, with each iteration's output becoming the next input. By merging and
contrasting the final reconstructed point cloud with the initial input, our
method successfully locates anomalies. Experiments show that IMRNet outperforms
previous state-of-the-art methods, achieving 66.1% in I-AUC on Anomaly-ShapeNet
dataset and 72.5% in I-AUC on Real3D-AD dataset. Our dataset will be released
at https://github.com/Chopper-233/Anomaly-ShapeNet
Related papers
- R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection [12.207437451118036]
3D anomaly detection plays a crucial role in monitoring parts for localized inherent defects in precision manufacturing.
Embedding-based and reconstruction-based approaches are among the most popular and successful methods.
We propose R3D-AD, reconstructing anomalous point clouds by diffusion model for precise 3D anomaly detection.
arXiv Detail & Related papers (2024-07-15T16:10:58Z) - Real3D-AD: A Dataset of Point Cloud Anomaly Detection [75.56719157477661]
We introduce Real3D-AD, a challenging high-precision point cloud anomaly detection dataset.
With 1,254 high-resolution 3D items from forty thousand to millions of points for each item, Real3D-AD is the largest dataset for high-precision 3D industrial anomaly detection.
We present a comprehensive benchmark for Real3D-AD, revealing the absence of baseline methods for high-precision point cloud anomaly detection.
arXiv Detail & Related papers (2023-09-23T00:43:38Z) - Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational
Autoencoder [10.097126085083827]
We present an end-to-end unsupervised anomaly detection framework for 3D point clouds.
We propose a deep variational autoencoder-based unsupervised anomaly detection network adapted to the 3D point cloud and an anomaly score specifically for 3D point clouds.
arXiv Detail & Related papers (2023-04-07T00:02:37Z) - 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) - Monocular Scene Reconstruction with 3D SDF Transformers [17.565474518578178]
We propose an SDF transformer network, which replaces the role of 3D CNN for better 3D feature aggregation.
Experiments on multiple datasets show that this 3D transformer network generates a more accurate and complete reconstruction.
arXiv Detail & Related papers (2023-01-31T09:54:20Z) - CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds [55.44204039410225]
We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D.
Our proposed method first generates some high-quality 3D proposals by leveraging the class-aware local group strategy on the object surface voxels.
To recover the features of missed voxels due to incorrect voxel-wise segmentation, we build a fully sparse convolutional RoI pooling module.
arXiv Detail & Related papers (2022-10-09T13:38:48Z) - Embracing Single Stride 3D Object Detector with Sparse Transformer [63.179720817019096]
In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases.
Many 3D detectors directly follow the common practice of 2D detectors, which downsample the feature maps even after quantizing the point clouds.
We propose Single-stride Sparse Transformer (SST) to maintain the original resolution from the beginning to the end of the network.
arXiv Detail & Related papers (2021-12-13T02:12:02Z) - H3D: Benchmark on Semantic Segmentation of High-Resolution 3D Point
Clouds and textured Meshes from UAV LiDAR and Multi-View-Stereo [4.263987603222371]
This paper introduces a 3D dataset which is unique in three ways.
It depicts the village of Hessigheim (Germany) henceforth referred to as H3D.
It is designed for promoting research in the field of 3D data analysis on one hand and to evaluate and rank emerging approaches.
arXiv Detail & Related papers (2021-02-10T09:33:48Z) - Local Grid Rendering Networks for 3D Object Detection in Point Clouds [98.02655863113154]
CNNs are powerful but it would be computationally costly to directly apply convolutions on point data after voxelizing the entire point clouds to a dense regular 3D grid.
We propose a novel and principled Local Grid Rendering (LGR) operation to render the small neighborhood of a subset of input points into a low-resolution 3D grid independently.
We validate LGR-Net for 3D object detection on the challenging ScanNet and SUN RGB-D datasets.
arXiv Detail & Related papers (2020-07-04T13:57:43Z) - A Nearest Neighbor Network to Extract Digital Terrain Models from 3D
Point Clouds [1.6249267147413524]
We present an algorithm that operates on 3D-point clouds and estimates the underlying DTM for the scene using an end-to-end approach.
Our model learns neighborhood information and seamlessly integrates this with point-wise and block-wise global features.
arXiv Detail & Related papers (2020-05-21T15:54:55Z) - Contextual-Bandit Anomaly Detection for IoT Data in Distributed
Hierarchical Edge Computing [65.78881372074983]
IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay.
We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems.
We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
arXiv Detail & Related papers (2020-04-15T06:13:33Z)
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.