A Large-Scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks

Kim, Sangpil; Chi, Hyung gun; Hu, Xiao; Huang, Qixing; Ramani, Karthik

doi:10.1007/978-3-030-58523-5_11

Cited by 48 publications

(17 citation statements)

References 32 publications

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“…We now use the mean persistence measure and Fréchet mean of persistence diagrams in a machine learning task of shape clustering on real-world data. The Mechanical Components Benchmark (MCB) is a large-scale dataset of 3D objects of mechanical components collected from online 3D computer aided design (CAD) repositories (Kim et al, 2020). MCB has 18,038 point cloud datasets with 25 classes.…”

Section: A Real-world Application: Shape Clusteringmentioning

confidence: 99%

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Approximating Persistent Homology for Large Datasets

Cao¹,

Monod²

2022

Preprint

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Persistent homology is an important methodology from topological data analysis which adapts theory from algebraic topology to data settings and has been successfully implemented in many applications. It produces a statistical summary in the form of a persistence diagram, which captures the shape and size of the data. Despite its widespread use, persistent homology is simply impossible to implement when a dataset is very large. In this paper we address the problem of finding a representative persistence diagram for prohibitively large datasets. We adapt the classical statistical method of bootstrapping, namely, drawing and studying smaller multiple subsamples from the large dataset. We show that the mean of the persistence diagrams of subsamples-taken as a mean persistence measure computed from the subsamples-is a valid approximation of the true persistent homology of the larger dataset. We give the rate of convergence of the mean persistence diagram to the true persistence diagram in terms of the number of subsamples and size of each subsample. Given the complex algebraic and geometric nature of persistent homology, we adapt the convexity and stability properties in the space of persistence diagrams together with random set theory to achieve our theoretical results for the general setting of point cloud data. We demonstrate our approach on simulated and real data, including an application of shape clustering on complex large-scale point cloud data.

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Section: A Real-world Application: Shape Clusteringmentioning

confidence: 99%

“…The point cloud imaging datasets 'Knot' and 'Lock' were obtained from the shape repository Digital Shape Workbench (http://visionair.ge.imati.cnr.it/). 'Bearing' and 'Motor' were obtained from the Mechanical Components Benchmark (Kim et al, 2020).…”

Section: Data and Software Availabilitymentioning

confidence: 99%

Approximating Persistent Homology for Large Datasets

Cao¹,

Monod²

2022

Preprint

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“…Sangpil et al [158] introduce a dataset of 58696 models of mechanical components from 68 classes called the Mechanical Components Benchmark (MCB) 7 . The MCB contains a hierarchical label tree grouping components into subclasses of different levels, such as Components → Fasteners → Nuts → Wingnuts, for example.…”

Section: D Object Datasetsmentioning

confidence: 99%

Deep Generative Models in Engineering Design: A Review

Regenwetter¹,

Nobari²,

Ahmed³

2021

Preprint

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Automated design synthesis has the potential to revolutionize the modern human design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative Machine Learning to design engineering may be the key to such automated design synthesis and is a research subject of great importance. We present a review and analysis of Deep Generative Learning models in engineering design. Deep Generative Models (DGMs) typically leverage deep networks to learn from an input dataset and learn to synthesize new designs. Recently, DGMs such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), feedforward Neural Networks (NNs) and certain Deep Reinforcement Learning (DRL) frameworks have shown promising results in design applications like structural optimization, materials design, and shape synthesis. The prevalence of DGMs in Engineering Design has skyrocketed since 2016. Anticipating continued growth, we conduct a review of recent advances with the hope of benefitting researchers interested in DGMs for design. We structure our review as an exposition of the algorithms, datasets, representation methods, and applications commonly used in the current literature. In particular, we discuss key works that have introduced new techniques and methods in DGMs, successfully applied DGMs to a design-related domain, or directly supported development of DGMs through datasets or auxiliary methods. We further identify key challenges and limitations currently seen in DGMs across design fields, such as design creativity, handling complex constraints and objectives, and modeling both form and functional performance simultaneously. In our discussion we identify possible solution pathways as key areas on which to target future work.

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“…For instance, generative design of a building facade would require a semantic segmentation annotation, while 3D reconstruction would require a 2D representation of a building as well as a 3D representation, the format of which could vary based on the researcher's approach (mesh, voxels, point cloud). The intricacy of the subject of interest makes the dataset creation time-consuming, so while there are quite a few 3D datasets (Sun et al, 2018, Chang et al, 2015, Wu et al, 2015, Kim et al, 2020, Xiang et al, 2014, Xiang et al, 2016, Mo et al, 2019, Koch et al, 2019, Lim et al, 2013 (Table 1), not many of them are related to architecture. The format issue makes this scarce number even smaller when applied to a specific problem, such as facade reconstruction, 3D classification or semantic segmentation of the building parts.…”

Section: Related Workmentioning

confidence: 99%

Synthetic Data Generation Pipeline for Geometric Deep Learning in Architecture

Fedorova

Tono²,

Nigam

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. With the growing interest in deep learning algorithms and computational design in the architectural field, the need for large, accessible and diverse architectural datasets increases. Due to the complexity of such 3D datasets, the most widespread techniques of 3D scanning and manual building modeling are very time-consuming, which does not allow to have a sufficiently large open-source dataset. We decided to tackle this problem by constructing a field-specific synthetic data generation pipeline that generates an arbitrary amount of 3D data along with the associated 2D and 3D annotations. The variety of annotations, the flexibility to customize the generated building and dataset parameters make this framework suitable for multiple deep learning tasks, including geometric deep learning that requires direct 3D supervision. Creating our building data generation pipeline we leveraged the experts’ architectural knowledge in order to construct a framework that would be modular, extendable and would provide a sufficient amount of class-balanced data samples. Moreover, we purposefully involve the researcher in the dataset customization allowing the introduction of additional building components, material textures, building classes, number and type of annotations as well as the number of views per 3D model sample. In this way, the framework would satisfy different research requirements and would be adaptable to a large variety of tasks. All code and data is made publicly available: https://cdinstitute.github.io/Building-Dataset-Generator/.

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A Large-Scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks

Cited by 48 publications

References 32 publications

Approximating Persistent Homology for Large Datasets

Approximating Persistent Homology for Large Datasets

Deep Generative Models in Engineering Design: A Review

Synthetic Data Generation Pipeline for Geometric Deep Learning in Architecture

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