PCGen: A Fully Parallelizable Point Cloud Generative Model
Generative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing. Current state-of-the-art point cloud random generation methods are not fast enough for these applications. We introduce a vector-quantized var...
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| Vydané v: | Sensors (Basel, Switzerland) Ročník 24; číslo 5; s. 1414 |
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| Hlavní autori: | , , , |
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| Jazyk: | English |
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| Abstract | Generative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing. Current state-of-the-art point cloud random generation methods are not fast enough for these applications. We introduce a vector-quantized variational autoencoder model (VQVAE) that can synthesize high-quality point clouds in milliseconds. Unlike previous work in VQVAEs, our model offers a compact sample representation suitable for conditional generation and data exploration with potential applications in rapid prototyping. We achieve this result by combining architectural improvements with an innovative approach for probabilistic random generation. First, we rethink current parallel point cloud autoencoder structures, and we propose several solutions to improve robustness, efficiency and reconstruction quality. Notable contributions in the decoder architecture include an innovative computation layer to process the shape semantic information, an attention mechanism that helps the model focus on different areas and a filter to cover possible sampling errors. Secondly, we introduce a parallel sampling strategy for VQVAE models consisting of a double encoding system, where a variational autoencoder learns how to generate the complex discrete distribution of the VQVAE, not only allowing quick inference but also describing the shape with a few global variables. We compare the proposed decoder and our VQVAE model with established and concurrent work, and we prove, one by one, the validity of the single contributions. |
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| AbstractList | Generative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing. Current state-of-the-art point cloud random generation methods are not fast enough for these applications. We introduce a vector-quantized variational autoencoder model (VQVAE) that can synthesize high-quality point clouds in milliseconds. Unlike previous work in VQVAEs, our model offers a compact sample representation suitable for conditional generation and data exploration with potential applications in rapid prototyping. We achieve this result by combining architectural improvements with an innovative approach for probabilistic random generation. First, we rethink current parallel point cloud autoencoder structures, and we propose several solutions to improve robustness, efficiency and reconstruction quality. Notable contributions in the decoder architecture include an innovative computation layer to process the shape semantic information, an attention mechanism that helps the model focus on different areas and a filter to cover possible sampling errors. Secondly, we introduce a parallel sampling strategy for VQVAE models consisting of a double encoding system, where a variational autoencoder learns how to generate the complex discrete distribution of the VQVAE, not only allowing quick inference but also describing the shape with a few global variables. We compare the proposed decoder and our VQVAE model with established and concurrent work, and we prove, one by one, the validity of the single contributions. Generative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing. Current state-of-the-art point cloud random generation methods are not fast enough for these applications. We introduce a vector-quantized variational autoencoder model (VQVAE) that can synthesize high-quality point clouds in milliseconds. Unlike previous work in VQVAEs, our model offers a compact sample representation suitable for conditional generation and data exploration with potential applications in rapid prototyping. We achieve this result by combining architectural improvements with an innovative approach for probabilistic random generation. First, we rethink current parallel point cloud autoencoder structures, and we propose several solutions to improve robustness, efficiency and reconstruction quality. Notable contributions in the decoder architecture include an innovative computation layer to process the shape semantic information, an attention mechanism that helps the model focus on different areas and a filter to cover possible sampling errors. Secondly, we introduce a parallel sampling strategy for VQVAE models consisting of a double encoding system, where a variational autoencoder learns how to generate the complex discrete distribution of the VQVAE, not only allowing quick inference but also describing the shape with a few global variables. We compare the proposed decoder and our VQVAE model with established and concurrent work, and we prove, one by one, the validity of the single contributions.Generative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing. Current state-of-the-art point cloud random generation methods are not fast enough for these applications. We introduce a vector-quantized variational autoencoder model (VQVAE) that can synthesize high-quality point clouds in milliseconds. Unlike previous work in VQVAEs, our model offers a compact sample representation suitable for conditional generation and data exploration with potential applications in rapid prototyping. We achieve this result by combining architectural improvements with an innovative approach for probabilistic random generation. First, we rethink current parallel point cloud autoencoder structures, and we propose several solutions to improve robustness, efficiency and reconstruction quality. Notable contributions in the decoder architecture include an innovative computation layer to process the shape semantic information, an attention mechanism that helps the model focus on different areas and a filter to cover possible sampling errors. Secondly, we introduce a parallel sampling strategy for VQVAE models consisting of a double encoding system, where a variational autoencoder learns how to generate the complex discrete distribution of the VQVAE, not only allowing quick inference but also describing the shape with a few global variables. We compare the proposed decoder and our VQVAE model with established and concurrent work, and we prove, one by one, the validity of the single contributions. |
| Audience | Academic |
| Author | Pižurica, Aleksandra Royen, Remco Vercheval, Nicolas Munteanu, Adrian |
| AuthorAffiliation | 1 Research Group for Artificial Intelligence and Sparse Modelling (GAIM), Department of Telecommunications and Information Processing, Faculty of Engineering and Architecture, Ghent University, 9000 Ghent, Belgium 2 Clifford Research Group, Department of Electronics and Information Systems, Faculty of Engineering and Architecture, Ghent University, 9000 Ghent, Belgium 3 Department of Electronics and Informatics (ETRO), Faculty of Engineering, Vrije Universiteit Brussel, 1050 Brussel, Belgium adrian.munteanu@vub.be (A.M.) |
| AuthorAffiliation_xml | – name: 3 Department of Electronics and Informatics (ETRO), Faculty of Engineering, Vrije Universiteit Brussel, 1050 Brussel, Belgium adrian.munteanu@vub.be (A.M.) – name: 2 Clifford Research Group, Department of Electronics and Information Systems, Faculty of Engineering and Architecture, Ghent University, 9000 Ghent, Belgium – name: 1 Research Group for Artificial Intelligence and Sparse Modelling (GAIM), Department of Telecommunications and Information Processing, Faculty of Engineering and Architecture, Ghent University, 9000 Ghent, Belgium |
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| Cites_doi | 10.1109/ICCV48922.2021.00577 10.1109/CVPR52688.2022.00614 10.1109/CVPR46437.2021.01481 10.1016/j.neunet.2018.09.001 10.1007/978-3-030-58574-7 10.1561/9781680836233 10.1109/ICCV.2019.01054 10.1007/978-3-031-19821-2 10.1109/TIP.2019.2957935 10.1007/978-3-030-58604-1 10.1109/ACCESS.2022.3196388 10.3390/buildings13092365 10.1109/SSCI47803.2020.9308513 10.1007/978-3-030-58580-8_22 10.1109/CVPR46437.2021.00286 10.1109/CVPRW56347.2022.00336 10.1109/CVPR52688.2022.00040 10.1109/CoG52621.2021.9619133 10.1007/978-3-030-01252-6 10.1109/CVPR.2018.00029 10.1109/CVPR.2018.00030 10.1109/CVPR.2019.00453 10.1007/978-1-4842-9579-3 10.1109/WACV45572.2020.9093430 10.1109/CVPR46437.2021.00737 |
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| References | ref_14 Cheng (ref_36) 2021; 34 ref_13 ref_35 ref_12 ref_34 ref_11 ref_33 ref_10 ref_32 ref_30 Charlier (ref_40) 2021; 22 ref_19 ref_18 ref_17 Chen (ref_25) 2020; 29 ref_39 ref_16 ref_38 ref_15 Phan (ref_9) 2018; 108 ref_24 ref_23 ref_45 ref_22 ref_44 ref_21 ref_43 ref_20 ref_42 ref_41 ref_1 ref_3 ref_2 ref_29 ref_28 ref_27 ref_26 Lin (ref_37) 2022; 10 ref_8 ref_5 ref_4 Li (ref_31) 2021; 40 ref_7 ref_6 |
| References_xml | – ident: ref_7 – ident: ref_44 doi: 10.1109/ICCV48922.2021.00577 – ident: ref_30 – ident: ref_35 doi: 10.1109/CVPR52688.2022.00614 – ident: ref_3 – ident: ref_24 – ident: ref_27 doi: 10.1109/CVPR46437.2021.01481 – ident: ref_26 – ident: ref_34 – volume: 108 start-page: 533 year: 2018 ident: ref_9 article-title: DGCNN: A convolutional neural network over large-scale labeled graphs publication-title: Neural Netw. doi: 10.1016/j.neunet.2018.09.001 – volume: 22 start-page: 74 year: 2021 ident: ref_40 article-title: Kernel Operations on the GPU, with Autodiff, without Memory Overflows publication-title: J. Mach. Learn. Res. – ident: ref_33 doi: 10.1007/978-3-030-58574-7 – ident: ref_10 doi: 10.1561/9781680836233 – ident: ref_11 – ident: ref_14 doi: 10.1109/ICCV.2019.01054 – ident: ref_17 doi: 10.1007/978-3-031-19821-2 – volume: 29 start-page: 3183 year: 2020 ident: ref_25 article-title: Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2019.2957935 – volume: 34 start-page: 6608 year: 2021 ident: ref_36 article-title: Learning 3d dense correspondence via canonical point autoencoder publication-title: Adv. Neural Inf. Process. Syst. – ident: ref_13 doi: 10.1007/978-3-030-58604-1 – volume: 10 start-page: 82076 year: 2022 ident: ref_37 article-title: Noise Point Detection From Airborne LiDAR Point Cloud Based on Spatial Hierarchical Directional Relationship publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3196388 – ident: ref_39 – ident: ref_38 doi: 10.3390/buildings13092365 – ident: ref_12 doi: 10.1109/SSCI47803.2020.9308513 – ident: ref_42 – ident: ref_1 – ident: ref_23 – ident: ref_28 doi: 10.1007/978-3-030-58580-8_22 – ident: ref_6 – ident: ref_8 – ident: ref_29 – ident: ref_45 doi: 10.1109/CVPR46437.2021.00286 – ident: ref_15 doi: 10.1109/CVPRW56347.2022.00336 – ident: ref_18 doi: 10.1109/CVPR52688.2022.00040 – ident: ref_41 – ident: ref_5 doi: 10.1109/CoG52621.2021.9619133 – ident: ref_16 doi: 10.1007/978-3-030-01252-6 – ident: ref_20 doi: 10.1109/CVPR.2018.00029 – ident: ref_22 doi: 10.1109/CVPR.2018.00030 – ident: ref_2 doi: 10.1109/CVPR.2019.00453 – ident: ref_19 – ident: ref_43 – volume: 40 start-page: 151 year: 2021 ident: ref_31 article-title: SP-GAN:Sphere-Guided 3D Shape Generation and Manipulation publication-title: ACM Trans. Graph. Proc. SIGGRAPH – ident: ref_4 doi: 10.1007/978-1-4842-9579-3 – ident: ref_32 doi: 10.1109/WACV45572.2020.9093430 – ident: ref_21 doi: 10.1109/CVPR46437.2021.00737 |
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| SubjectTerms | autoencoder Euclidean space Machine learning point clouds real-time computing Semantics variational autoencoder vector-quantized variational autoencoder Virtual reality |
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| Title | PCGen: A Fully Parallelizable Point Cloud Generative Model |
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