mlsanityData to 14 Oct 2026

Z2P: Instant Visualization of Point Clouds

arXiv 2105.14548 ↗First posted 30 May 2021cs.GR cs.CV cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

We present a technique for visualizing point clouds using a neural network. Our technique allows for an instant preview of any point cloud, and bypasses the notoriously difficult surface reconstruction problem or the need to estimate oriented normals for splat-based rendering. We cast the preview problem as a conditional image-to-image translation task, and design a neural network that translates point depth-map directly into an image, where the point cloud is visualized as though a surface was reconstructed from it. Furthermore, the resulting appearance of the visualized point cloud can be, optionally, conditioned on simple control variables (e.g., color and light). We demonstrate that our technique instantly produces plausible images, and can, on-the-fly effectively handle noise, non-uniform sampling, and thin surfaces sheets.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 27 of its 55 references that are in the map. First posted on arXiv 30 May 2021.

Builds on

27 of 55 references.

References · 27

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  3. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  4. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
  5. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 488
  6. 2018PCN: Point Completion Networkcited by 384
  7. 2019Neural Point-Based Graphicscited by 327
  8. 2020Learning Gradient Fields for Shape Generationcited by 245
  9. 2019Differentiable Surface Splatting for Point-based Geometry Processingcited by 239
  10. 2019Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderercited by 221
  11. 2018PU-Net: Point Cloud Upsampling Networkcited by 209
  12. 2018Patch-based Progressive 3D Point Set Upsamplingcited by 101
15 more
  1. 2019PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Cloudscited by 99
  2. 2017PCPNET: Learning Local Shape Properties from Raw Point Cloudscited by 98
  3. 2018EC-Net: an Edge-aware Point set Consolidation Networkcited by 84
  4. 2018Point Cloud GANcited by 79
  5. 2019Neural Point Cloud Rendering via Multi-Plane Projectioncited by 60
  6. 2016Deep Shading: Convolutional Neural Networks for Screen-Space Shadingcited by 46
  7. 2021Style-based Point Generator with Adversarial Rendering for Point Cloud Completioncited by 45
  8. 2018RenderNet: A deep convolutional network for differentiable rendering from 3D shapescited by 42
  9. 2020Point2Mesh: A Self-Prior for Deformable Meshescited by 41
  10. 2020PointGMM: a Neural GMM Network for Point Cloudscited by 33
  11. 2021Orienting Point Clouds with Dipole Propagationcited by 31
  12. 2020MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglementcited by 15
  13. 2020Self-Sampling for Neural Point Cloud Consolidationcited by 15
  14. 2020TRANSPR: Transparency Ray-Accumulating Neural 3D Scene Point Renderercited by 9
  15. 2018Deep-learning the Latent Space of Light Transportcited by 5

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.

3 papers.

  1. 2022PointVST: Self-Supervised Pre-training for 3D Point Clouds via View-Specific Point-to-Image Translationcited by 7
  2. 2021Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase Codingcited by 5
  3. 2024Robust 3D Point Clouds Classification based on Declarative Defenderscited by 2