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Generative AI models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design.
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 1901
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Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition
Chu Wang, Marcello Pelillo, and Kaleem Siddiqi · 1906
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MeshSDF: Differentiable Iso-Surface Extraction
Edoardo Remelli, Artem Lukoianov, Stephan R. Richter, Benoît Guillard, Timur Bagautdinov, Pierre Baque, and Pascal Fua · 2006
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A convolutional learning system for object classification in 3-D lidar data
Danil Prokhorov · 2010
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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VoxNet: A 3D Convolutional Neural Network for real-time object recognition
Daniel Maturana and Sebastian Scherer · 2015
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Geodesic convolutional neural networks on Riemannian manifolds
Jonathan Masci, Davide Boscaini, Michael M. Bronstein, and Pierre Vandergheynst · 2015
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Multi-view convolutional neural networks for 3D shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller · 2015
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3D ShapeNets: A Deep Representation for Volumetric Shapes, 2015
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Voting for voting in online point cloud object detection
Dominic Zeng Wang and Ingmar Posner · 2015
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ShapeNet: An Information-Rich 3D Model Repository
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
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PointNet: A 3D Convolutional Neural Network for real-time object class recognition, 2016
A. Garcia-Garcia, F. Gomez-Donoso, J. Garcia-Rodriguez, S. Orts-Escolano, M. Cazorla, and J. Azorin-Lopez · 2016
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Volumetric and Multi-View CNNs for Object Classification on 3D Data
Charles R. Qi, Hao Su, Matthias Niessner, Angela Dai, Mengyuan Yan, and Leonidas J. Guibas · 2016
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Learning shape correspondence with anisotropic convolutional neural networks
Davide Boscaini, Jonathan Masci, Emanuele Rodolà, and Michael Bronstein · 2016
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FPNN: Field Probing Neural Networks for 3D Data
Yangyan Li, Soeren Pirk, Hao Su, Charles R. Qi, and Leonidas J. Guibas · 2016
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Rethinking the Inception Architecture for Computer Vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Aggregated Residual Transformations for Deep Neural Networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2016
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Octree Generating Networks: Efficient Convolutional Architectures for High-Resolution 3D Outputs
Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox · 2017
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Learning Representations and Generative Models for 3D Point Clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2017
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SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Heinrich Müller · 2017
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Convolutional neural networks on surfaces via seamless toric covers
Haggai Maron, Meirav Galun, Noam Aigerman, Miri Trope, Nadav Dym, Ersin Yumer, Vladimir G. Kim, and Yaron Lipman · 2017
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SO-Net: Self-Organizing Network for Point Cloud Analysis
Jiaxin Li, Ben M. Chen, and Gim Hee Lee · 2018
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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Multi-resolution 3D CNN for learning multi-scale spatial features in CAD models
Sambit Ghadai, Xian Yeow Lee, Aditya Balu, Soumik Sarkar, and Adarsh Krishnamurthy · 2021
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Text2Mesh: Text-Driven Neural Stylization for Meshes
Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, and Rana Hanocka · 2021
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ClipMatrix: Text-controlled Creation of 3D Textured Meshes
Nikolay Jetchev · 2021
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Deep Learning for Real-Time Aerodynamic Evaluations of Arbitrary Vehicle Shapes
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Learning three-dimensional flow for interactive aerodynamic design
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Multi-chart Generative Surface Modeling
Heli Ben-Hamu, Haggai Maron, Itay Kezurer, Gal Avineri, and Yaron Lipman · 2018
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Learning Implicit Fields for Generative Shape Modeling
Zhiqin Chen and Hao Zhang · 2018
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Geodesic Convolutional Shape Optimization
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Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows
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A Generative Design and Drag Coefficient Prediction System for Sedan Car Side Silhouettes based on Computational Fluid Dynamics
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An augmented reality platform for interactive aerodynamic design and analysis
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DEBOSH: Deep Bayesian Shape Optimization
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Exploiting Generative Models for Performance Predictions of 3D Car Designs
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Improving multi-modal learning with uni-modal teachers
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A Predictive and Generative Design Approach for Three-Dimensional Mesh Shapes Using Target-Embedding Variational Autoencoder
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LION: Latent Point Diffusion Models for 3D Shape Generation
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Point-E: A System for Generating 3D Point Clouds from Complex Prompts
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XDGAN: Multi-Modal 3D Shape Generation in 2D Space
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Pre-train, Self-train, Distill: A simple recipe for Supersizing 3D Reconstruction
Kalyan Vasudev Alwala, Abhinav Gupta, and Shubham Tulsiani · 2022
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ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation
Zhengzhe Liu, Peng Dai, Ruihui Li, Xiaojuan Qi, and Chi-Wing Fu · 2022
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Surrogate drag model of non-spherical fragments based on artificial neural networks
Dajun Xin, Junsheng Zeng, and Kun Xue · 2022
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Interactive design of 2D car profiles with aerodynamic feedback
Nicolas Rosset, Guillaume Cordonnier, Regis Duvigneau, Adrien Bousseau, and Nicolas Rosset Guillaume Cordonnier Regis Duvigneau Adrien Bousseau · 2023
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ATTENTION-ENHANCED MULTIMODAL LEARNING FOR CONCEPTUAL DESIGN EVALUATIONS
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