6,426 research outputs found
3D Object Recognition Based on Volumetric Representation Using Convolutional Neural Networks
Following the success of Convolutional Neural Networks on object recognition and image classification using 2D images; in this work the framework has been extended to process 3D data. However, many current systems require huge amount of computation cost for dealing with large amount of data. In this work, we introduce an efficient 3D volumetric representation for training and testing CNNs and we also build several datasets based on the volumetric representation of 3D digits, different rotations along the x, y and z axis are also taken into account. Unlike the normal volumetric representation, our datasets are much less memory usage. Finally, we introduce a model based on the combination of CNN models, the structure of the model is based on the classical LeNet. The accuracy result achieved is beyond the state of art and it can classify a 3D digit in around 9 ms
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels
With the advent of affordable depth sensors, 3D capture becomes more and more
ubiquitous and already has made its way into commercial products. Yet,
capturing the geometry or complete shapes of everyday objects using scanning
devices (e.g. Kinect) still comes with several challenges that result in noise
or even incomplete shapes. Recent success in deep learning has shown how to
learn complex shape distributions in a data-driven way from large scale 3D CAD
Model collections and to utilize them for 3D processing on volumetric
representations and thereby circumventing problems of topology and
tessellation. Prior work has shown encouraging results on problems ranging from
shape completion to recognition. We provide an analysis of such approaches and
discover that training as well as the resulting representation are strongly and
unnecessarily tied to the notion of object labels. Thus, we propose a full
convolutional volumetric auto encoder that learns volumetric representation
from noisy data by estimating the voxel occupancy grids. The proposed method
outperforms prior work on challenging tasks like denoising and shape
completion. We also show that the obtained deep embedding gives competitive
performance when used for classification and promising results for shape
interpolation
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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