mlsanityData to 14 Oct 2026

Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results

arXiv 2112.12053 ↗First posted 22 Dec 2021cs.CVcited by 9

Not on the map. Why?

Abstract (arXiv)

As real-scanned point clouds are mostly partial due to occlusions and viewpoints, reconstructing complete 3D shapes based on incomplete observations becomes a fundamental problem for computer vision. With a single incomplete point cloud, it becomes the partial point cloud completion problem. Given multiple different observations, 3D reconstruction can be addressed by performing partial-to-partial point cloud registration. Recently, a large-scale Multi-View Partial (MVP) point cloud dataset has been released, which consists of over 100,000 high-quality virtual-scanned partial point clouds. Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration. In total, 128 participants registered for the competition, and 31 teams made valid submissions. The top-ranked solutions will be analyzed, and then we will discuss future research directions.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 27 of its 36 references that are in the map. First posted on arXiv 22 Dec 2021.

Builds on

27 of 36 references.

References · 27

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  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
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  11. 2021SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformercited by 164
  12. 2019PRNet: Self-Supervised Learning for Partial-to-Partial Registrationcited by 146
15 more
  1. 2019Morphing and Sampling Network for Dense Point Cloud Completioncited by 132
  2. 2020Cascaded Refinement Network for Point Cloud Completioncited by 112
  3. 2021Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completioncited by 112
  4. 2020Point Cloud Completion by Skip-attention Network with Hierarchical Foldingcited by 106
  5. 2021Variational Relational Point Completion Networkcited by 104
  6. 2021A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completioncited by 100
  7. 2020PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Pathscited by 97
  8. 2021Robust Point Cloud Registration Framework Based on Deep Graph Matchingcited by 97
  9. 2020DeepGMR: Learning Latent Gaussian Mixture Models for Registrationcited by 91
  10. 2021Point Cloud Upsampling via Disentangled Refinementcited by 74
  11. 2020Detail Preserved Point Cloud Completion via Separated Feature Aggregationcited by 70
  12. 2021Unsupervised 3D Shape Completion through GAN Inversioncited by 70
  13. 2021Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Codingcited by 50
  14. 2020Correspondence Matrices are Underratedcited by 5
  15. 2021Point Cloud Registration using Representative Overlapping Pointscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 3; 2025: 0; 2026: 1.

9 papers.

  1. 2023AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformerscited by 67
  2. 20233DSGrasp: 3D Shape-Completion for Robotic Graspcited by 13
  3. 2023MV-DeepSDF: Implicit Modeling with Multi-Sweep Point Clouds for 3D Vehicle Reconstruction in Autonomous Drivingcited by 11
  4. 2024Diffusion Models in 3D Vision: A Surveycited by 11
  5. 2023SCARP: 3D Shape Completion in ARbitrary Poses for Improved Graspingcited by 8
  6. 2024Linear Partial Gromov-Wasserstein Embeddingcited by 1
  7. 2024Curvature Informed Furthest Point Samplingcited by 1
  8. 2023DualGenerator: Information Interaction-based Generative Network for Point Cloud Completioncited by 0
  9. 2026SUMI: Scalable Unified Model for 3D Point Cloud Inferencecited by 0