Generative Models for 3D Point Clouds
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Abstract (arXiv)
Point clouds are rich geometric data structures, where their three dimensional structure offers an excellent domain for understanding the representation learning and generative modeling in 3D space. In this work, we aim to improve the performance of point cloud latent-space generative models by experimenting with transformer encoders, latent-space flow models, and autoregressive decoders. We analyze and compare both generation and reconstruction performance of these models on various object types.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 15 of its 18 references that are in the map. First posted on arXiv 26 Feb 2023.
Builds on
15 of 18 references.
References · 15
- 2017Attention Is All You Needcited by 60,361
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
- 2015Variational Inference with Normalizing Flowscited by 2,170
- 2017Deep Setscited by 1,421
- 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
- 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 746
- 2016Improving Variational Inference with Inverse Autoregressive Flowcited by 640
- 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 488
- 2019Modeling Point Clouds with Self-Attention and Gumbel Subset Samplingcited by 151
- 2016Neural Autoregressive Distribution Estimationcited by 95
3 more
- 2018Transformation Autoregressive Networkscited by 27
- 2018Iterative Transformer Network for 3D Point Cloudcited by 11
- 2018Pioneer Networks: Progressively Growing Generative Autoencodercited by 4
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.