Quaternion Equivariant Capsule Networks for 3D Point Clouds
- URL: http://arxiv.org/abs/1912.12098v3
- Date: Sun, 23 Aug 2020 13:12:46 GMT
- Title: Quaternion Equivariant Capsule Networks for 3D Point Clouds
- Authors: Yongheng Zhao, Tolga Birdal, Jan Eric Lenssen, Emanuele Menegatti,
Leonidas Guibas, Federico Tombari
- Abstract summary: We present a 3D capsule module for processing point clouds that is equivariant to 3D rotations and translations.
We connect dynamic routing between capsules to the well-known Weiszfeld algorithm.
Based on our operator, we build a capsule network that disentangles geometry from pose.
- Score: 58.566467950463306
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a 3D capsule module for processing point clouds that is
equivariant to 3D rotations and translations, as well as invariant to
permutations of the input points. The operator receives a sparse set of local
reference frames, computed from an input point cloud and establishes end-to-end
transformation equivariance through a novel dynamic routing procedure on
quaternions. Further, we theoretically connect dynamic routing between capsules
to the well-known Weiszfeld algorithm, a scheme for solving \emph{iterative
re-weighted least squares} (IRLS) problems with provable convergence
properties. It is shown that such group dynamic routing can be interpreted as
robust IRLS rotation averaging on capsule votes, where information is routed
based on the final inlier scores. Based on our operator, we build a capsule
network that disentangles geometry from pose, paving the way for more
informative descriptors and a structured latent space. Our architecture allows
joint object classification and orientation estimation without explicit
supervision of rotations. We validate our algorithm empirically on common
benchmark datasets.
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) - CpT: Convolutional Point Transformer for 3D Point Cloud Processing [10.389972581905]
We present CpT: Convolutional point Transformer - a novel deep learning architecture for dealing with the unstructured nature of 3D point cloud data.
CpT is an improvement over existing attention-based Convolutions Neural Networks as well as previous 3D point cloud processing transformers.
Our model can serve as an effective backbone for various point cloud processing tasks when compared to the existing state-of-the-art approaches.
arXiv Detail & Related papers (2021-11-21T17:45:55Z) - DeltaConv: Anisotropic Point Cloud Learning with Exterior Calculus [13.18401177210079]
We introduce a new convolution operator called DeltaConv, which combines geometric operators from exterior calculus to enable the construction of anisotropic filters on point clouds.
Our convolutions are robust and simple to implement and show improved accuracy compared to state-of-the-art approaches on several benchmarks.
arXiv Detail & Related papers (2021-11-16T21:58:55Z) - UPDesc: Unsupervised Point Descriptor Learning for Robust Registration [54.95201961399334]
UPDesc is an unsupervised method to learn point descriptors for robust point cloud registration.
We show that our learned descriptors yield superior performance over existing unsupervised methods.
arXiv Detail & Related papers (2021-08-05T17:11:08Z) - 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 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) - DV-ConvNet: Fully Convolutional Deep Learning on Point Clouds with
Dynamic Voxelization and 3D Group Convolution [0.7340017786387767]
3D point cloud interpretation is a challenging task due to the randomness and sparsity of the component points.
In this work, we draw attention back to the standard 3D convolutions towards an efficient 3D point cloud interpretation.
Our network is able to run and converge at a considerably fast speed, while yields on-par or even better performance compared with the state-of-the-art methods on several benchmark datasets.
arXiv Detail & Related papers (2020-09-07T07:45:05Z) - 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.