69 research outputs found
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
Point Anywhere: Directed Object Estimation from Omnidirectional Images
One of the intuitive instruction methods in robot navigation is a pointing
gesture. In this study, we propose a method using an omnidirectional camera to
eliminate the user/object position constraint and the left/right constraint of
the pointing arm. Although the accuracy of skeleton and object detection is low
due to the high distortion of equirectangular images, the proposed method
enables highly accurate estimation by repeatedly extracting regions of interest
from the equirectangular image and projecting them onto perspective images.
Furthermore, we found that training the likelihood of the target object in
machine learning further improves the estimation accuracy.Comment: Accepted to SIGGRAPH 2023 Poster. Project page:
https://github.com/NKotani/PointAnywher
Tactile Estimation of Extrinsic Contact Patch for Stable Placement
Precise perception of contact interactions is essential for the fine-grained
manipulation skills for robots. In this paper, we present the design of
feedback skills for robots that must learn to stack complex-shaped objects on
top of each other. To design such a system, a robot should be able to reason
about the stability of placement from very gentle contact interactions. Our
results demonstrate that it is possible to infer the stability of object
placement based on tactile readings during contact formation between the object
and its environment. In particular, we estimate the contact patch between a
grasped object and its environment using force and tactile observations to
estimate the stability of the object during a contact formation. The contact
patch could be used to estimate the stability of the object upon the release of
the grasp. The proposed method is demonstrated on various pairs of objects that
are used in a very popular board game.Comment: Under submissio
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