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Patch-based Progressive 3D Point Set Upsampling

arXiv 1811.11286 ↗First posted 27 Nov 2018cs.CV cs.GR cs.LGcited by 101

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Abstract (arXiv)

We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we progressively train a cascade of patch-based upsampling networks on different levels of detail end-to-end. We propose a series of architectural design contributions that lead to a substantial performance boost. The effect of each technical contribution is demonstrated in an ablation study. Qualitative and quantitative experiments show that our method significantly outperforms the state-of-the-art learning-based and optimazation-based approaches, both in terms of handling low-resolution inputs and revealing high-fidelity details.

Key facts. Cited by 101 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Nov 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 25; 2022: 26; 2023: 17; 2024: 14; 2025: 17; 2026: 2.

101 papers.

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  1. 2021Multimodal Virtual Point 3D Detectioncited by 166
  2. 2021Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Mergingcited by 104
  3. 20213D AffordanceNet: A Benchmark for Visual Object Affordance Understandingcited by 100
  4. 2021SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generationcited by 93
  5. 2021Score-Based Point Cloud Denoisingcited by 83
  6. 20213D Semantic Scene Completion: a Surveycited by 74
  7. 2021Point Cloud Upsampling via Disentangled Refinementcited by 74
  8. 2021Deep Implicit Moving Least-Squares Functions for 3D Reconstructioncited by 67
  9. 2022PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Pathscited by 57
  10. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  11. 2022Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Surveycited by 54
  12. 2021Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsamplingcited by 48
38 more
  1. 2021View-Guided Point Cloud Completioncited by 46
  2. 2023Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functionscited by 43
  3. 2022Density-preserving Deep Point Cloud Compressioncited by 39
  4. 2022Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformercited by 38
  5. 2023Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projectioncited by 36
  6. 2021PU-Transformer: Point Cloud Upsampling Transformercited by 32
  7. 2021Orienting Point Clouds with Dipole Propagationcited by 31
  8. 2022GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representationcited by 28
  9. 2023A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsamplingcited by 28
  10. 2021Deep Point Set Resampling via Gradient Fieldscited by 24
  11. 2022Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representationcited by 24
  12. 2023Advancements in Point Cloud Data Augmentation for Deep Learning: A Surveycited by 24
  13. 2024CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformerscited by 21
  14. 2025SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorizationcited by 20
  15. 2022Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysiscited by 19
  16. 2023Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsamplingcited by 19
  17. 2022PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsamplingcited by 18
  18. 2021PU-Flow: a Point Cloud Upsampling Network with Normalizing Flowscited by 17
  19. 2021SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Renderingcited by 17
  20. 2022PU-EVA: An Edge Vector based Approximation Solution for Flexible-scale Point Cloud Upsamplingcited by 16
  21. 2021Deep Point Cloud Reconstructioncited by 15
  22. 2022Point Cloud Upsampling via Cascaded Refinement Networkcited by 14
  23. 2021PC2-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsamplingcited by 11
  24. 2022Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detectioncited by 10
  25. 2023Test-Time Adaptation for Point Cloud Upsampling Using Meta-Learningcited by 10
  26. 2024Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Renderingcited by 9
  27. 2021RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2D-Tree Representationcited by 8
  28. 2023Lidar Upsampling with Sliced Wasserstein Distancecited by 8
  29. 2022Arbitrary Point Cloud Upsampling with Spherical Mixture of Gaussianscited by 6
  30. 2021RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoisingcited by 5
  31. 2024Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learningcited by 5
  32. 2025SRMamba: Mamba for Super-Resolution of LiDAR Point Cloudscited by 5
  33. 2022HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Drivingcited by 4
  34. 2022Upsampling Autoencoder for Self-Supervised Point Cloud Learningcited by 4
  35. 2023iPUNet:Iterative Cross Field Guided Point Cloud Upsamplingcited by 4
  36. 2023DiffPMAE: Diffusion Masked Autoencoders for Point Cloud Reconstructioncited by 4
  37. 2024Fast Point Cloud Geometry Compression with Context-based Residual Coding and INR-based Refinementcited by 4
  38. 2024Deep Learning for 3D Point Cloud Enhancement: A Surveycited by 4

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