1,591 research outputs found
Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
Recent progresses in 3D deep learning has shown that it is possible to design
special convolution operators to consume point cloud data. However, a typical
drawback is that rotation invariance is often not guaranteed, resulting in
networks being trained with data augmented with rotations. In this paper, we
introduce a novel convolution operator for point clouds that achieves rotation
invariance. Our core idea is to use low-level rotation invariant geometric
features such as distances and angles to design a convolution operator for
point cloud learning. The well-known point ordering problem is also addressed
by a binning approach seamlessly built into the convolution. This convolution
operator then serves as the basic building block of a neural network that is
robust to point clouds under 6DoF transformations such as translation and
rotation. Our experiment shows that our method performs with high accuracy in
common scene understanding tasks such as object classification and
segmentation. Compared to previous works, most importantly, our method is able
to generalize and achieve consistent results across different scenarios in
which training and testing can contain arbitrary rotations.Comment: International Conference on 3D Vision (3DV) 201
Global Context Aware Convolutions for 3D Point Cloud Understanding
Recent advances in deep learning for 3D point clouds have shown great
promises in scene understanding tasks thanks to the introduction of convolution
operators to consume 3D point clouds directly in a neural network. Point cloud
data, however, could have arbitrary rotations, especially those acquired from
3D scanning. Recent works show that it is possible to design point cloud
convolutions with rotation invariance property, but such methods generally do
not perform as well as translation-invariant only convolution. We found that a
key reason is that compared to point coordinates, rotation-invariant features
consumed by point cloud convolution are not as distinctive. To address this
problem, we propose a novel convolution operator that enhances feature
distinction by integrating global context information from the input point
cloud to the convolution. To this end, a globally weighted local reference
frame is constructed in each point neighborhood in which the local point set is
decomposed into bins. Anchor points are generated in each bin to represent
global shape features. A convolution can then be performed to transform the
points and anchor features into final rotation-invariant features. We conduct
several experiments on point cloud classification, part segmentation, shape
retrieval, and normals estimation to evaluate our convolution, which achieves
state-of-the-art accuracy under challenging rotations
Learning SO(3) Equivariant Representations with Spherical CNNs
We address the problem of 3D rotation equivariance in convolutional neural
networks. 3D rotations have been a challenging nuisance in 3D classification
tasks requiring higher capacity and extended data augmentation in order to
tackle it. We model 3D data with multi-valued spherical functions and we
propose a novel spherical convolutional network that implements exact
convolutions on the sphere by realizing them in the spherical harmonic domain.
Resulting filters have local symmetry and are localized by enforcing smooth
spectra. We apply a novel pooling on the spectral domain and our operations are
independent of the underlying spherical resolution throughout the network. We
show that networks with much lower capacity and without requiring data
augmentation can exhibit performance comparable to the state of the art in
standard retrieval and classification benchmarks.Comment: Camera-ready. Accepted to ECCV'18 as oral presentatio
3D Point Capsule Networks
In this paper, we propose 3D point-capsule networks, an auto-encoder designed
to process sparse 3D point clouds while preserving spatial arrangements of the
input data. 3D capsule networks arise as a direct consequence of our novel
unified 3D auto-encoder formulation. Their dynamic routing scheme and the
peculiar 2D latent space deployed by our approach bring in improvements for
several common point cloud-related tasks, such as object classification, object
reconstruction and part segmentation as substantiated by our extensive
evaluations. Moreover, it enables new applications such as part interpolation
and replacement.Comment: As published in CVPR 2019 (camera ready version), with supplementary
materia
3D Point Capsule Networks
In this paper, we propose 3D point-capsule networks, an auto-encoder designed
to process sparse 3D point clouds while preserving spatial arrangements of the
input data. 3D capsule networks arise as a direct consequence of our novel
unified 3D auto-encoder formulation. Their dynamic routing scheme and the
peculiar 2D latent space deployed by our approach bring in improvements for
several common point cloud-related tasks, such as object classification, object
reconstruction and part segmentation as substantiated by our extensive
evaluations. Moreover, it enables new applications such as part interpolation
and replacement
- …