REVNET: Rotation-Equivariant Point Cloud Completion via Vector Neuron Anchor Transformer

• URL: http://arxiv.org/abs/2601.08558v1
• Date: Tue, 13 Jan 2026 13:42:11 GMT
• Title: REVNET: Rotation-Equivariant Point Cloud Completion via Vector Neuron Anchor Transformer
• Authors: Zhifan Ni, Eckehard Steinbach,
• Abstract summary: REVNET is a novel framework for robust point cloud completion under arbitrary rotations.<n>Our method outperforms state-of-the-art approaches on the synthetic MVP dataset in the equivariant setting.<n>The source code will be released on GitHub under URL: https://github.com/nizhf/REVNET.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incomplete point clouds captured by 3D sensors often result in the loss of both geometric and semantic information. Most existing point cloud completion methods are built on rotation-variant frameworks trained with data in canonical poses, limiting their applicability in real-world scenarios. While data augmentation with random rotations can partially mitigate this issue, it significantly increases the learning burden and still fails to guarantee robust performance under arbitrary poses. To address this challenge, we propose the Rotation-Equivariant Anchor Transformer (REVNET), a novel framework built upon the Vector Neuron (VN) network for robust point cloud completion under arbitrary rotations. To preserve local details, we represent partial point clouds as sets of equivariant anchors and design a VN Missing Anchor Transformer to predict the positions and features of missing anchors. Furthermore, we extend VN networks with a rotation-equivariant bias formulation and a ZCA-based layer normalization to improve feature expressiveness. Leveraging the flexible conversion between equivariant and invariant VN features, our model can generate point coordinates with greater stability. Experimental results show that our method outperforms state-of-the-art approaches on the synthetic MVP dataset in the equivariant setting. On the real-world KITTI dataset, REVNET delivers competitive results compared to non-equivariant networks, without requiring input pose alignment. The source code will be released on GitHub under URL: https://github.com/nizhf/REVNET.

Related papers

• A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features [60.76577388438418]
  3D anomaly detection (AD) is a crucial task in computer vision, aiming to identify anomalous points or regions from point cloud data.<n>Existing methods may encounter challenges when handling point clouds with changes in orientation and position because the resulting features may vary significantly.<n>We propose a novel Rotationally Invariant Features (RIF) framework for 3D AD, which maps each point into a rotationally invariant space to maintain consistency of representation.
  arXiv  Detail & Related papers  (2025-11-17T08:16:05Z)
• Surfel-based 3D Registration with Equivariant SE(3) Features [34.796697445601914]
  Point cloud registration is crucial for ensuring 3D alignment consistency of multiple local point clouds in 3D reconstruction for remote sensing or digital heritage.<n>We propose a novel surfel-based pose learning regression approach to address these issues.<n>Our method can initialize surfels from Lidar point cloud using virtual perspective camera parameters, and learns explicit $mathbfSE(3)$ equivariant features.
  arXiv  Detail & Related papers  (2025-08-28T13:53:44Z)
• PointRWKV: Efficient RWKV-Like Model for Hierarchical Point Cloud Learning [56.14518823931901]
  We present PointRWKV, a model of linear complexity derived from the RWKV model in the NLP field. We first propose to explore the global processing capabilities within PointRWKV blocks using modified multi-headed matrix-valued states. To extract local geometric features simultaneously, we design a parallel branch to encode the point cloud efficiently in a fixed radius near-neighbors graph with a graph stabilizer.
  arXiv  Detail & Related papers  (2024-05-24T05:02:51Z)
• Rotation-Invariant Transformer for Point Cloud Matching [42.5714375149213]
  We introduce RoITr, a Rotation-Invariant Transformer to cope with the pose variations in the point cloud matching task. We propose a global transformer with rotation-invariant cross-frame spatial awareness learned by the self-attention mechanism. RoITr surpasses the existing methods by at least 13 and 5 percentage points in terms of Inlier Ratio and Registration Recall.
  arXiv  Detail & Related papers  (2023-03-14T20:55:27Z)
• SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation [65.4396959244269]
  The paper tackles the challenge by designing a general framework to construct 3D learning architectures. The proposed approach can be applied to general backbones like PointNet and DGCNN. Experiments on ModelNet40, ShapeNet, and the real-world dataset ScanObjectNN, demonstrated that the method achieves a great trade-off between efficiency, rotation, and accuracy.
  arXiv  Detail & Related papers  (2022-09-13T12:12:19Z)
• 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)
• Improving the Sample-Complexity of Deep Classification Networks with Invariant Integration [77.99182201815763]
  Leveraging prior knowledge on intraclass variance due to transformations is a powerful method to improve the sample complexity of deep neural networks. We propose a novel monomial selection algorithm based on pruning methods to allow an application to more complex problems. We demonstrate the improved sample complexity on the Rotated-MNIST, SVHN and CIFAR-10 datasets.
  arXiv  Detail & Related papers  (2022-02-08T16:16:11Z)
• Training or Architecture? How to Incorporate Invariance in Neural Networks [14.162739081163444]
  We propose a method for provably invariant network architectures with respect to group actions. In a nutshell, we intend to 'undo' any possible transformation before feeding the data into the actual network. We analyze properties of such approaches, extend them to equivariant networks, and demonstrate their advantages in terms of robustness as well as computational efficiency in several numerical examples.
  arXiv  Detail & Related papers  (2021-06-18T10:31:00Z)
• 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)
• A Rotation-Invariant Framework for Deep Point Cloud Analysis [132.91915346157018]
  We introduce a new low-level purely rotation-invariant representation to replace common 3D Cartesian coordinates as the network inputs. Also, we present a network architecture to embed these representations into features, encoding local relations between points and their neighbors, and the global shape structure. We evaluate our method on multiple point cloud analysis tasks, including shape classification, part segmentation, and shape retrieval.
  arXiv  Detail & Related papers  (2020-03-16T14:04:45Z)

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.