BiEquiFormer: Bi-Equivariant Representations for Global Point Cloud Registration

• URL: http://arxiv.org/abs/2407.08729v2
• Date: Wed, 14 Aug 2024 01:46:33 GMT
• Title: BiEquiFormer: Bi-Equivariant Representations for Global Point Cloud Registration
• Authors: Stefanos Pertigkiozoglou, Evangelos Chatzipantazis, Kostas Daniilidis,
• Abstract summary: The goal of this paper is to address the problem of global point cloud registration (PCR) i.e., finding the optimal alignment between point clouds. We show that state-of-the-art deep learning methods suffer from huge performance degradation when the point clouds are arbitrarily placed in space.
• Score: 28.75341781515012
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The goal of this paper is to address the problem of global point cloud registration (PCR) i.e., finding the optimal alignment between point clouds irrespective of the initial poses of the scans. This problem is notoriously challenging for classical optimization methods due to computational constraints. First, we show that state-of-the-art deep learning methods suffer from huge performance degradation when the point clouds are arbitrarily placed in space. We propose that equivariant deep learning should be utilized for solving this task and we characterize the specific type of bi-equivariance of PCR. Then, we design BiEquiformer a novel and scalable bi-equivariant pipeline i.e. equivariant to the independent transformations of the input point clouds. While a naive approach would process the point clouds independently we design expressive bi-equivariant layers that fuse the information from both point clouds. This allows us to extract high-quality superpoint correspondences and in turn, robust point-cloud registration. Extensive comparisons against state-of-the-art methods show that our method achieves comparable performance in the canonical setting and superior performance in the robust setting in both the 3DMatch and the challenging low-overlap 3DLoMatch dataset.

Related papers

• Point Cloud Pre-training with Diffusion Models [62.12279263217138]
  We propose a novel pre-training method called Point cloud Diffusion pre-training (PointDif) PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification, segmentation and detection.
  arXiv  Detail & Related papers  (2023-11-25T08:10:05Z)
• Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding [61.30276576646909]
  We propose an augmentation-free unsupervised approach for point clouds to learn transferable point-level features via soft clustering, named SoftClu. We exploit the affiliation of points to their clusters as a proxy to enable self-training through a pseudo-label prediction task.
  arXiv  Detail & Related papers  (2022-10-06T10:18:16Z)
• Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation [78.6612285236938]
  We propose a novel DAT (textbfDual textbfAdaptive textbfTransformations) model for weakly supervised point cloud segmentation. We evaluate our proposed DAT model with two popular backbones on the large-scale S3DIS and ScanNet-V2 datasets.
  arXiv  Detail & Related papers  (2022-07-19T05:43:14Z)
• E2PN: Efficient SE(3)-Equivariant Point Network [12.520265159777255]
  This paper proposes a convolution structure for learning SE(3)-equivariant features from 3D point clouds. It can be viewed as an equivariant version of kernel point convolutions (KPConv), a widely used convolution form to process point cloud data.
  arXiv  Detail & Related papers  (2022-06-11T02:15:46Z)
• A Representation Separation Perspective to Correspondences-free Unsupervised 3D Point Cloud Registration [40.12490804387776]
  3D point cloud registration in remote sensing field has been greatly advanced by deep learning based methods. We propose a correspondences-free unsupervised point cloud registration (UPCR) method from the representation separation perspective. Our method not only filters out the disturbance in pose-invariant representation but also is robust to partial-to-partial point clouds or noise.
  arXiv  Detail & Related papers  (2022-03-24T17:50:19Z)
• RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning [32.18566879365623]
  3D point clouds deep learning is a promising field of research that allows a neural network to learn features of point clouds directly. We propose a simple yet effective convolution operator that enhances feature distinction by designing powerful rotation invariant features from the local regions. Our network architecture can capture both local and global context by simply tuning the neighborhood size in each convolution layer.
  arXiv  Detail & Related papers  (2022-02-26T08:32:44Z)
• Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud Classification [3.710922682020501]
  This work offers a new strategy for learning powerful representations via a contrastive learning approach that can be embedded into any point cloud classification network. Our method achieves 82.9% accuracy on the real-world ScanObjectNN dataset and substantial performance gains up to 2.9% in DCGNN, 3.1% in PointNet++, and 2.4% in GBNet.
  arXiv  Detail & Related papers  (2022-01-27T09:10:28Z)
• Correspondence-Free Point Cloud Registration with SO(3)-Equivariant Implicit Shape Representations [12.343333815270402]
  The proposed shape registration method achieves three major advantages through combining equivariant feature learning with implicit shape models. Results show superior performance compared with existing correspondence-free deep registration methods.
  arXiv  Detail & Related papers  (2021-07-21T18:18:21Z)
• PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features [91.2054994193218]
  We propose a point-set learning framework PRIN, focusing on rotation invariant feature extraction in point clouds analysis. In addition, we extend PRIN to a sparse version called SPRIN, which directly operates on sparse point clouds. Results show that, on the dataset with randomly rotated point clouds, SPRIN demonstrates better performance than state-of-the-art methods without any data augmentation.
  arXiv  Detail & Related papers  (2021-02-24T06:44:09Z)
• Deep Magnification-Flexible Upsampling over 3D Point Clouds [103.09504572409449]
  We propose a novel end-to-end learning-based framework to generate dense point clouds. We first formulate the problem explicitly, which boils down to determining the weights and high-order approximation errors. Then, we design a lightweight neural network to adaptively learn unified and sorted weights as well as the high-order refinements.
  arXiv  Detail & Related papers  (2020-11-25T14:00:18Z)
• Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds [145.79324955896845]
  We propose a permutable anisotropic convolutional operation (PAI-Conv) that calculates soft-permutation matrices for each point. Experiments on point clouds demonstrate that PAI-Conv produces competitive results in classification and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2020-05-27T02:42:29Z)

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.