11,458 research outputs found
CloudWalker: Random walks for 3D point cloud shape analysis
Point clouds are gaining prominence as a method for representing 3D shapes,
but their irregular structure poses a challenge for deep learning methods. In
this paper we propose CloudWalker, a novel method for learning 3D shapes using
random walks. Previous works attempt to adapt Convolutional Neural Networks
(CNNs) or impose a grid or mesh structure to 3D point clouds. This work
presents a different approach for representing and learning the shape from a
given point set. The key idea is to impose structure on the point set by
multiple random walks through the cloud for exploring different regions of the
3D object. Then we learn a per-point and per-walk representation and aggregate
multiple walk predictions at inference. Our approach achieves state-of-the-art
results for two 3D shape analysis tasks: classification and retrieval
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
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
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