DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction

Itai Lang, Dvir Ginzburg, Shai Avidan, Dan Raviv

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We present a new method for real-time non-rigid dense correspondence between point clouds based on structured shape construction. Our method,termed Deep Point Correspondence (DPC),requires a fraction of the training data compared to previous techniques and presents better generalization capabilities. Until now,two main approaches have been suggested for the dense correspondence problem. The first is a spectral-based approach that obtains great results on synthetic datasets but requires mesh connectivity of the shapes and long inference processing time while being unstable in real-world scenarios. The second is a spatial approach that uses an encoder-decoder framework to regress an ordered point cloud for the matching alignment from an irregular input. Unfortunately,the decoder brings considerable disadvantages,as it requires a large amount of training data and struggles to generalize well in cross-dataset evaluations. DPC's novelty lies in its lack of a decoder component. Instead,we use latent similarity and the input coordinates themselves to construct the point cloud and determine correspondence,replacing the coordinate regression done by the decoder. Extensive experiments show that our construction scheme leads to a performance boost in comparison to recent state-of-the-art correspondence methods. Our code is publicly available1.

Original languageEnglish
Title of host publicationProceedings - 2021 International Conference on 3D Vision, 3DV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781665426886
StatePublished - 2021
Event9th International Conference on 3D Vision, 3DV 2021 - Virtual, Online, United Kingdom
Duration: 1 Dec 20213 Dec 2021

Publication series

NameProceedings - 2021 International Conference on 3D Vision, 3DV 2021


Conference9th International Conference on 3D Vision, 3DV 2021
Country/TerritoryUnited Kingdom
CityVirtual, Online


  • 3D Point Clouds
  • Dense Correspondence
  • Non Rigid Shapes
  • Real Time
  • Unsupervised Deep Learning

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition


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