DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction

• URL: http://arxiv.org/abs/2110.08636v1
• Date: Sat, 16 Oct 2021 18:41:13 GMT
• Title: DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction
• Authors: Itai Lang, Dvir Ginzburg, Shai Avidan, Dan Raviv
• Abstract summary: 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. Our construction scheme leads to a performance boost in comparison to recent state-of-the-art correspondence methods.
• Score: 29.191330510706408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 available at https://github.com/dvirginz/DPC.

Related papers

• Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors [53.6277160912059]
  We propose a method to promote pros of data-driven based and overfitting-based methods for better generalization, faster inference, and higher accuracy in learning neural SDFs. We introduce a novel statistical reasoning algorithm in local regions which is able to finetune data-driven based priors without signed distance supervision, clean point cloud, or point normals.
  arXiv  Detail & Related papers  (2024-10-25T16:48:44Z)
• PointRegGPT: Boosting 3D Point Cloud Registration using Generative Point-Cloud Pairs for Training [90.06520673092702]
  We present PointRegGPT, boosting 3D point cloud registration using generative point-cloud pairs for training. To our knowledge, this is the first generative approach that explores realistic data generation for indoor point cloud registration.
  arXiv  Detail & Related papers  (2024-07-19T06:29:57Z)
• NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching [31.61255365182462]
  We present Neural Correspondence Prior (NCP), a new paradigm for computing correspondences between 3D shapes. Our approach is fully unsupervised and can lead to high-quality correspondences even in challenging cases. We show that NCP is data-efficient, fast, and state-of-the-art results on many tasks.
  arXiv  Detail & Related papers  (2023-01-14T07:22:18Z)
• Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version) [13.286247750893681]
  3D point cloud registration is a fundamental problem in computer vision and robotics. We propose a novel deep graph matching-based framework for point cloud registration.
  arXiv  Detail & Related papers  (2022-11-09T06:05:25Z)
• IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment [58.8330387551499]
  We formulate the problem as estimation of point-wise trajectories (i.e., smooth curves) We propose IDEA-Net, an end-to-end deep learning framework, which disentangles the problem under the assistance of the explicitly learned temporal consistency. We demonstrate the effectiveness of our method on various point cloud sequences and observe large improvement over state-of-the-art methods both quantitatively and visually.
  arXiv  Detail & Related papers  (2022-03-22T10:14:08Z)
• Unsupervised 3D Human Mesh Recovery from Noisy Point Clouds [30.401088478228235]
  We present an unsupervised approach to reconstruct human shape and pose from noisy point cloud. Our network is trained from scratch with no need to warm-up the network with supervised data.
  arXiv  Detail & Related papers  (2021-07-15T18:07:47Z)
• Robust Point Cloud Registration Framework Based on Deep Graph Matching [5.865029600972316]
  3D point cloud registration is a fundamental problem in computer vision and robotics. We propose a novel deep graph matchingbased framework for point cloud registration.
  arXiv  Detail & Related papers  (2021-03-07T04:20:29Z)
• CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds [48.22275177437932]
  This paper addresses the problem of computing dense correspondence between 3D shapes in the form of point clouds. We propose CorrNet3D -- the first unsupervised and end-to-end deep learning-based framework.
  arXiv  Detail & Related papers  (2020-12-31T14:55:51Z)
• Cascaded Refinement Network for Point Cloud Completion with Self-supervision [74.80746431691938]
  We introduce a two-branch network for shape completion. The first branch is a cascaded shape completion sub-network to synthesize complete objects. The second branch is an auto-encoder to reconstruct the original partial input.
  arXiv  Detail & Related papers  (2020-10-17T04:56:22Z)
• RPM-Net: Robust Point Matching using Learned Features [79.52112840465558]
  RPM-Net is a less sensitive and more robust deep learning-based approach for rigid point cloud registration. Unlike some existing methods, our RPM-Net handles missing correspondences and point clouds with partial visibility.
  arXiv  Detail & Related papers  (2020-03-30T13:45:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.