mlsanityData to 7 Oct 2026

Rethinking Data Input for Point Cloud Upsampling

arXiv 2407.04476 ↗First posted 5 Jul 2024cs.CV cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

Point cloud upsampling is crucial for tasks like 3D reconstruction. While existing methods rely on patch-based inputs, and there is no research discussing the differences and principles between point cloud model full input and patch based input. Ergo, we propose a novel approach using whole model inputs i.e. Average Segment input. Our experiments on PU1K and ABC datasets reveal that patch-based inputs consistently outperform whole model inputs. To understand this, we will delve into factors in feature extraction, and network architecture that influence upsampling results.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 31 references that are in the map. First posted on arXiv 5 Jul 2024.

Builds on

19 of 31 references.

References · 19

  1. 2017Attention Is All You Needcited by 59,932
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  5. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  6. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  7. 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 339
  8. 2018PU-Net: Point Cloud Upsampling Networkcited by 208
  9. 2019PU-GAN: a Point Cloud Upsampling Adversarial Networkcited by 162
  10. 2018Patch-based Progressive 3D Point Set Upsamplingcited by 101
  11. 2019PU-GCN: Point Cloud Upsampling using Graph Convolutional Networkscited by 100
  12. 2021Point Cloud Upsampling via Disentangled Refinementcited by 74
7 more
  1. 2023Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functionscited by 43
  2. 2023Point2CAD: Reverse Engineering CAD Models from 3D Point Cloudscited by 33
  3. 2021PU-Transformer: Point Cloud Upsampling Transformercited by 32
  4. 2021ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completioncited by 28
  5. 2018Indoor Scene Understanding in 2.5/3D for Autonomous Agents: A Surveycited by 25
  6. 2022Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representationcited by 24
  7. 2021PU-Flow: a Point Cloud Upsampling Network with Normalizing Flowscited by 17

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Representation Learning of Point Cloud Upsampling in Global and Local Inputscited by 1
  2. 2025Med-PU: Point Cloud Upsampling for High-Fidelity 3D Medical Shape Reconstructioncited by 0