General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment

• URL: http://arxiv.org/abs/2302.09907v3
• Date: Tue, 10 Oct 2023 03:55:14 GMT
• Title: General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment
• Authors: Liang Xie, Yibo Yang, Wenxiao Wang, Binbin Lin, Deng Cai, Xiaofei He, Ronghua Liang
• Abstract summary: We propose Weight-Feature Alignment (WFA) to construct a local Invariant Reference Frame (IRF) Our WFA algorithm provides a general solution for the point clouds of all scenes.
• Score: 40.421478916432676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compared to 2D images, 3D point clouds are much more sensitive to rotations. We expect the point features describing certain patterns to keep invariant to the rotation transformation. There are many recent SOTA works dedicated to rotation-invariant learning for 3D point clouds. However, current rotation-invariant methods lack generalizability on the point clouds in the open scenes due to the reliance on the global distribution, \ie the global scene and backgrounds. Considering that the output activation is a function of the pattern and its orientation, we need to eliminate the effect of the orientation.In this paper, inspired by the idea that the network weights can be considered a set of points distributed in the same 3D space as the input points, we propose Weight-Feature Alignment (WFA) to construct a local Invariant Reference Frame (IRF) via aligning the features with the principal axes of the network weights. Our WFA algorithm provides a general solution for the point clouds of all scenes. WFA ensures the model achieves the target that the response activity is a necessary and sufficient condition of the pattern matching degree. Practically, we perform experiments on the point clouds of both single objects and open large-range scenes. The results suggest that our method almost bridges the gap between rotation invariance learning and normal methods.

Related papers

• CRIN: Rotation-Invariant Point Cloud Analysis and Rotation Estimation via Centrifugal Reference Frame [60.24797081117877]
  We propose the CRIN, namely Centrifugal Rotation-Invariant Network. CRIN directly takes the coordinates of points as input and transforms local points into rotation-invariant representations. A continuous distribution for 3D rotations based on points is introduced.
  arXiv  Detail & Related papers  (2023-03-06T13:14:10Z)
• PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration [16.75367717130046]
  State-of-the-art models are not robust to rotations, which remains an unknown prior to real applications. We introduce a novel Patch-wise Rotation-invariant network (PaRot) Our disentanglement module extracts high-quality rotation-robust features and the proposed lightweight model achieves competitive results.
  arXiv  Detail & Related papers  (2023-02-06T02:13:51Z)
• Rethinking Rotation Invariance with Point Cloud Registration [18.829454172955202]
  We propose an effective framework for rotation invariance learning via three sequential stages, namely rotation-invariant shape encoding, aligned feature integration, and deep feature registration. Experimental results on 3D shape classification, part segmentation, and retrieval tasks prove the feasibility of our work.
  arXiv  Detail & Related papers  (2022-12-31T08:17:09Z)
• 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)
• 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)
• Adjoint Rigid Transform Network: Task-conditioned Alignment of 3D Shapes [86.2129580231191]
  Adjoint Rigid Transform (ART) Network is a neural module which can be integrated with a variety of 3D networks. ART learns to rotate input shapes to a learned canonical orientation, which is crucial for a lot of tasks. We will release our code and pre-trained models for further research.
  arXiv  Detail & Related papers  (2021-02-01T20:58:45Z)
• Deep Positional and Relational Feature Learning for Rotation-Invariant Point Cloud Analysis [107.9979381402172]
  We propose a rotation-invariant deep network for point clouds analysis. The network is hierarchical and relies on two modules: a positional feature embedding block and a relational feature embedding block. Experiments show state-of-the-art classification and segmentation performances on benchmark datasets.
  arXiv  Detail & Related papers  (2020-11-18T04:16:51Z)
• Global Context Aware Convolutions for 3D Point Cloud Understanding [32.953907994511376]
  We propose a novel convolution operator that enhances feature distinction by integrating global context information from the input point cloud to the convolution. A convolution can then be performed to transform the points and anchor features into final rotation-invariant features.
  arXiv  Detail & Related papers  (2020-08-07T04:33:27Z)
• 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.