R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection
- URL: http://arxiv.org/abs/2407.10862v1
- Date: Mon, 15 Jul 2024 16:10:58 GMT
- Title: R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection
- Authors: Zheyuan Zhou, Le Wang, Naiyu Fang, Zili Wang, Lemiao Qiu, Shuyou Zhang,
- Abstract summary: 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.
- Score: 12.207437451118036
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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. However, there are two major challenges to the practical application of the current approaches: 1) the embedded models suffer the prohibitive computational and storage due to the memory bank structure; 2) the reconstructive models based on the MAE mechanism fail to detect anomalies in the unmasked regions. In this paper, we propose R3D-AD, reconstructing anomalous point clouds by diffusion model for precise 3D anomaly detection. Our approach capitalizes on the data distribution conversion of the diffusion process to entirely obscure the input's anomalous geometry. It step-wisely learns a strict point-level displacement behavior, which methodically corrects the aberrant points. To increase the generalization of the model, we further present a novel 3D anomaly simulation strategy named Patch-Gen to generate realistic and diverse defect shapes, which narrows the domain gap between training and testing. Our R3D-AD ensures a uniform spatial transformation, which allows straightforwardly generating anomaly results by distance comparison. Extensive experiments show that our R3D-AD outperforms previous state-of-the-art methods, achieving 73.4% Image-level AUROC on the Real3D-AD dataset and 74.9% Image-level AUROC on the Anomaly-ShapeNet dataset with an exceptional efficiency.
Related papers
- 3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly [22.150521360544744]
We propose a new large-scale anomaly detection dataset called 3CAD.
3CAD includes eight different types of manufactured parts, totaling 27,039 high- resolution images labeled with pixel-level anomalies.
This is the largest and first anomaly de-tection dataset dedicated to 3C product quality control.
arXiv Detail & Related papers (2025-02-09T03:37:54Z) - Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation [18.917672392645006]
2D diffusion models trained on 2D slices are starting to be leveraged for 3D MRI reconstruction.
Existing methods pertain to a fixed voxel size, and performance degrades when the voxel size is varied.
We propose and study several approaches for resolution-robust 3D MRI reconstruction with 2D diffusion priors.
arXiv Detail & Related papers (2024-12-24T18:25:50Z) - DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models [67.50989119438508]
We introduce DSplats, a novel method that directly denoises multiview images using Gaussian-based Reconstructors to produce realistic 3D assets.
Our experiments demonstrate that DSplats not only produces high-quality, spatially consistent outputs, but also sets a new standard in single-image to 3D reconstruction.
arXiv Detail & Related papers (2024-12-11T07:32:17Z) - RIGI: Rectifying Image-to-3D Generation Inconsistency via Uncertainty-aware Learning [27.4552892119823]
inconsistencies in multi-view snapshots frequently introduce noise and artifacts along object boundaries, undermining the 3D reconstruction process.
We leverage 3D Gaussian Splatting (3DGS) for 3D reconstruction, and explicitly integrate uncertainty-aware learning into the reconstruction process.
We apply adaptive pixel-wise loss weighting to regularize the models, reducing reconstruction intensity in high-uncertainty regions.
arXiv Detail & Related papers (2024-11-28T02:19:28Z) - DM3D: Distortion-Minimized Weight Pruning for Lossless 3D Object Detection [42.07920565812081]
We propose a novel post-training weight pruning scheme for 3D object detection.
It determines redundant parameters in the pretrained model that lead to minimal distortion in both locality and confidence.
This framework aims to minimize detection distortion of network output to maximally maintain detection precision.
arXiv Detail & Related papers (2024-07-02T09:33:32Z) - Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets.
We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
arXiv Detail & Related papers (2024-01-06T07:30:41Z) - 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) - Detecting Rotated Objects as Gaussian Distributions and Its 3-D
Generalization [81.29406957201458]
Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects.
We argue that such a mechanism has fundamental limitations in building an effective regression loss for rotation detection.
We propose to model the rotated objects as Gaussian distributions.
We extend our approach from 2-D to 3-D with a tailored algorithm design to handle the heading estimation.
arXiv Detail & Related papers (2022-09-22T07:50:48Z) - Towards Model Generalization for Monocular 3D Object Detection [57.25828870799331]
We present an effective unified camera-generalized paradigm (CGP) for Mono3D object detection.
We also propose the 2D-3D geometry-consistent object scaling strategy (GCOS) to bridge the gap via an instance-level augment.
Our method called DGMono3D achieves remarkable performance on all evaluated datasets and surpasses the SoTA unsupervised domain adaptation scheme.
arXiv Detail & Related papers (2022-05-23T23:05:07Z) - Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image.
Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space.
We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step.
This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
arXiv Detail & Related papers (2020-08-31T17:10:48Z)
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.