10,282 research outputs found
ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids
We introduce an unsupervised feature learning approach that embeds 3D shape
information into a single-view image representation. The main idea is a
self-supervised training objective that, given only a single 2D image, requires
all unseen views of the object to be predictable from learned features. We
implement this idea as an encoder-decoder convolutional neural network. The
network maps an input image of an unknown category and unknown viewpoint to a
latent space, from which a deconvolutional decoder can best "lift" the image to
its complete viewgrid showing the object from all viewing angles. Our
class-agnostic training procedure encourages the representation to capture
fundamental shape primitives and semantic regularities in a data-driven
manner---without manual semantic labels. Our results on two widely-used shape
datasets show 1) our approach successfully learns to perform "mental rotation"
even for objects unseen during training, and 2) the learned latent space is a
powerful representation for object recognition, outperforming several existing
unsupervised feature learning methods.Comment: To appear at ECCV 201
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints
We propose a Convolutional Neural Network (CNN)-based model "RotationNet,"
which takes multi-view images of an object as input and jointly estimates its
pose and object category. Unlike previous approaches that use known viewpoint
labels for training, our method treats the viewpoint labels as latent
variables, which are learned in an unsupervised manner during the training
using an unaligned object dataset. RotationNet is designed to use only a
partial set of multi-view images for inference, and this property makes it
useful in practical scenarios where only partial views are available. Moreover,
our pose alignment strategy enables one to obtain view-specific feature
representations shared across classes, which is important to maintain high
accuracy in both object categorization and pose estimation. Effectiveness of
RotationNet is demonstrated by its superior performance to the state-of-the-art
methods of 3D object classification on 10- and 40-class ModelNet datasets. We
also show that RotationNet, even trained without known poses, achieves the
state-of-the-art performance on an object pose estimation dataset. The code is
available on https://github.com/kanezaki/rotationnetComment: 24 pages, 23 figures. Accepted to CVPR 201
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