ABC: A Big CAD Model Dataset for Geometric Deep Learning

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

doi:10.1109/cvpr.2019.00983

Cited by 318 publications

(246 citation statements)

References 44 publications

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“…In this experiment, we randomly select 80% data for training and 20% for testing, and use the training data to fine-tune the network pre-trained on ShapeNet. Our method performs the best on both ABC [58] and Thingi10K [59]. This demonstrates that our method generalizes well to new datasets, producing minimum average SDE and dealing well with more complex and asymmetric shapes.…”

Section: Results and Evaluationsmentioning

confidence: 76%

“…In Table 2, we also report accuracy comparison of our method with alternative methods (in terms of SDE as no ground truth is available) on ABC [58] and Thingi10K [59] datasets, which contain a large number of asymmetric shapes. In this experiment, we randomly select 80% data for training and 20% for testing, and use the training data to fine-tune the network pre-trained on ShapeNet.…”

Section: Results and Evaluationsmentioning

confidence: 99%

“…The symmetry distance error (×10 −4 ) of different methods on ABC[58] and Thingi10K[59]. Our method produces minimum average SDE and can deal with more complex and asymmetric shapes.…”

mentioning

confidence: 99%

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PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models

Gao

Zhang

Meng

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion. Thus it is an important problem to analyze various symmetry forms of 3D shapes. Planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes. In this paper, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape. Our framework trains an unsupervised 3D convolutional neural network to extract global model features and then outputs possible global symmetry parameters, where input shapes are represented using voxels. We introduce a dedicated symmetry distance loss along with a regularization loss to avoid generating duplicated symmetry planes. Our network can also identify generalized cylinders by predicting their rotation axes. We further provide a method to remove invalid and duplicated planes and axes. We demonstrate that our method is able to produce reliable and accurate results. Our neural network based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling. Our method is also robust even with noisy or incomplete input surfaces.

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Section: Results and Evaluationsmentioning

confidence: 76%

Section: Results and Evaluationsmentioning

confidence: 99%

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PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models

Gao

Zhang

Meng

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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“…CAD files in most cases only depict the desired object. To get this data, there are already large databases that have high quality [11]. Because CAD is an industry-standard, companies can also use their existing data-sets.…”

Section: Results Evaluation and Limitationsmentioning

confidence: 99%

Towards Artificial Intelligence Serving as an Inspiring Co-Creation Partner

German¹,

Limm²,

Wölfel³

et al. 2019

EAI Endorsed Transactions on Creative Technologies

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The promise of artificial intelligence (AI), in particular its latest developments in deep learning, has been influencing all kinds of disciplines such as engineering, business, agriculture, and humanities. More recently it also includes disciplines that were "reserved" to humans such as art and design. While there is a strong debate going on if creativity is profoundly human, we want to investigate if creativity can be enhanced by AI-not replaced. To be an inspiring co-creation partner by suggesting unexpected design variations and by learning the designer's taste. To do so we adopted AI algorithms, which can be trained by a small sample set of shapes of a given object, to propose novel shapes. The evaluation of our proposed methods revealed that it can be used by trained designers as well as non-designers to support the design process in different phases and that it could lead to novel designs not intended/foreseen. Besides the potentials of AI, we also point out and discuss moral threads caused by the latest developments in AI with respect to the creative sector.

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“…In the area of 2D and 3D computer graphics and geometry processing, directional fields have been utilized for a vast number of applications, such as mesh generation using distinct 3D data modalities [4,6], texture mapping [16], and image-based tracing of line drawings [2], to name a few. Appearing datasets [10] open more possibilities for this setup with various problems to solve. With the guidance of an appropriately designed directional field, both topological (e.g., placement of singularity points) and geometric (e.g., smoothness) properties of the underlying geometric structure may be efficiently derived.…”

Section: Introductionmentioning

confidence: 99%

Learning to Approximate Directional Fields Defined Over 2D Planes

Taktasheva

Matveev

Artemov

et al. 2019

Lecture Notes in Computer Science

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Reconstruction of directional fields is a need in many geometry processing tasks, such as image tracing, extraction of 3D geometric features, and finding principal surface directions. A common approach to the construction of directional fields from data relies on complex optimization procedures, which are usually poorly formalizable, require a considerable computational effort, and do not transfer across applications. In this work, we propose a deep learning-based approach and study the expressive power and generalization ability.

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ABC: A Big CAD Model Dataset for Geometric Deep Learning

Cited by 318 publications

References 44 publications

PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models

PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models

Towards Artificial Intelligence Serving as an Inspiring Co-Creation Partner

Learning to Approximate Directional Fields Defined Over 2D Planes

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