ABC: A Big CAD Model Dataset For Geometric Deep Learning

Koch, Sebastian; Matveev, Albert; Jiang, Zhongshi; Williams, Francis H.; Artemov, Alexey; Burnaev, Evgeny; Alexa, Marc; Zorin, Denis; Panozzo, Daniele

doi:10.48550/arxiv.1812.06216

Cited by 5 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, deep learning-based AFR methods often require large-scale three-dimensional CAD model datasets with labeled machining features to train neural networks. Currently, there are large CAD model datasets available, such as ABC dataset 48 and MCB dataset 49 , but they do not contain the labels required for machining feature recognition tasks. Customized 3D CAD model datasets are accessible 24,27,30 , but they only considered machined parts.…”

Section: Overview Of the Smcad Datasetmentioning

confidence: 99%

Adaptive recognition of machining features in sheet metal parts based on a graph class-incremental learning strategy

Ma,

Yang

2024

Sci Rep

View full text Add to dashboard Cite

The integration of computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM) systems is significantly enhanced by employing deep learning-based automatic feature recognition (AFR) methods. These methods outperform traditional, rule-based approaches, particularly in handling the complexities of intersecting features. However, existing deep learning-based AFR methods face two major challenges. The initial challenge stems from the frequent utilization of voxelized or point-cloud representations of CAD models, resulting in the unfortunate loss of valuable geometric and topological information inherent in original Boundary representation (B-Rep) models. The second challenge involves the limitation of supervised deep learning methods in identifying machining features that are not present in the predefined dataset. This constraint renders them suboptimal for the continually evolving datasets of real industrial scenarios. To address the first challenge, this study introduces a graph-structured language, Multidimensional Attributed Face-Edge Graph (maFEG), crafted to encapsulate the intricate geometric and topological details of CAD models. Furthermore, a graph neural network, Sheet-metalNet, is proposed for the efficient learning and interpretation of maFEGs. To tackle the second challenge, a three-component incremental learning strategy is proposed: an initial phase of pre-training and fine-tuning, a prototype sampling-based replay, and a stage employing knowledge distillation for parameter regularization. The effectiveness of Sheet-metalNet and its complementary incremental learning strategy is evaluated using the open-source MFCAD++ dataset and the newly created SMCAD dataset. Experimental results show that Sheet-metalNet surpasses state-of-the-art AFR methods in machining feature recognition accuracy. Moreover, Sheet-metalNet demonstrates adaptability to dynamic dataset changes, maintaining high performance when encountering newly introduced features, thanks to its innovative incremental learning strategy.

show abstract

Section: Overview Of the Smcad Datasetmentioning

confidence: 99%

Adaptive recognition of machining features in sheet metal parts based on a graph class-incremental learning strategy

Ma,

Yang

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In all the experiments in this work, we basically followed the protocols in UNDC [10] and conducted training on the ABC [42] dataset. The ABC dataset consists of more than 30,000 CAD mesh models, with sharp boundaries and diverse curve surfaces that are suitable for examining high-quality, high-resolution reconstruction tasks.…”

Section: Datasetsmentioning

confidence: 99%

“…Both are real scanned point clouds. Our network was trained only on the ABC[42] dataset without any specific training on similar scanned data, indicating its remarkable generalization and transferability.…”

mentioning

confidence: 99%

High-Resolution and Efficient Neural Dual Contouring for Surface Reconstruction from Point Clouds

et al. 2023

View full text Add to dashboard Cite

The surface mesh reconstruction from point clouds has been a fundamental research topic in Computer Vision and Computer Graphics. Recently, the Neural Implicit Representation (NIR)-based reconstruction has revolutionized this research topic. This work summarizes and analyzes representative works on NIR-based reconstruction and highlights several important insights. However, one major problem with existing works is that they struggle to handle high-resolution meshes. To address this, this paper introduces HRE-NDC, a novel High-Resolution and Efficient Neural Dual Contouring approach for mesh reconstruction from point clouds, which takes the previous state-of-the-art as a baseline and adopts a coarse-to-fine network structure design, along with feature-preserving downsampling and other improvements. HRE-NDC significantly reduces training time and memory usage while achieving better surface reconstruction results. Experimental results demonstrate the superiority of our method in both visualization and quantitative results, and it shows excellent generalization performance on various data including large indoor scenes, real-scanned urban buildings, clothing, and human bodies.

show abstract

“…We present experimental results on two widely-used datasets of manmade objects, namely, ABCParts [9] and Thingi10K [46]. ABCParts is a subset of the ABC dataset [47], which is considered a standard benchmark for learning-based primitive detection methods [9,10,48] in recent times. It comprises the point cloud of 30k CAD models, where each point cloud has 10, 000 points and at least one curved surface.…”

Section: Datasetsmentioning

confidence: 99%

Sharp Feature-Preserving 3D Mesh Reconstruction from Point Clouds Based on Primitive Detection

Liu,

Xu,

Xiao

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

High-fidelity mesh reconstruction from point clouds has long been a fundamental research topic in computer vision and computer graphics. Traditional methods require dense triangle meshes to achieve high fidelity, but excessively dense triangles may lead to unnecessary storage and computational burdens, while also struggling to capture clear, sharp, and continuous edges. This paper argues that the key to high-fidelity reconstruction lies in preserving sharp features. Therefore, we introduce a novel sharp-feature-preserving reconstruction framework based on primitive detection. It includes an improved deep-learning-based primitive detection module and two novel mesh splitting and selection modules that we propose. Our framework can accurately and reasonably segment primitive patches, fit meshes in each patch, and split overlapping meshes at the triangle level to ensure true sharpness while obtaining lightweight mesh models. Quantitative and visual experimental results demonstrate that our framework outperforms both the state-of-the-art learning-based primitive detection methods and traditional reconstruction methods. Moreover, our designed modules are plug-and-play, which not only apply to learning-based primitive detectors but also can be combined with other point cloud processing tasks such as edge extraction or random sample consensus (RANSAC) to achieve high-fidelity results.

show abstract

ABC: A Big CAD Model Dataset For Geometric Deep Learning

Cited by 5 publications

References 0 publications

Adaptive recognition of machining features in sheet metal parts based on a graph class-incremental learning strategy

Adaptive recognition of machining features in sheet metal parts based on a graph class-incremental learning strategy

High-Resolution and Efficient Neural Dual Contouring for Surface Reconstruction from Point Clouds

Sharp Feature-Preserving 3D Mesh Reconstruction from Point Clouds Based on Primitive Detection

Contact Info

Product

Resources

About