Review of multi-view 3D object recognition methods based on deep learning
Abstract Three-dimensional (3D) object recognition is widely used in automated driving,
medical image analysis, virtual/augmented reality, artificial intelligence robots, and other …
medical image analysis, virtual/augmented reality, artificial intelligence robots, and other …
A review on deep learning approaches for 3D data representations in retrieval and classifications
AS Gezawa, Y Zhang, Q Wang, L Yunqi - IEEE access, 2020 - ieeexplore.ieee.org
Deep learning approach has been used extensively in image analysis tasks. However,
implementing the methods in 3D data is a bit complex because most of the previously …
implementing the methods in 3D data is a bit complex because most of the previously …
Rotationnet: Joint object categorization and pose estimation using multiviews from unsupervised viewpoints
A Kanezaki, Y Matsushita… - Proceedings of the IEEE …, 2018 - openaccess.thecvf.com
Abstract 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 …
which takes multi-view images of an object as input and jointly estimates its pose and object …
A survey on deep learning advances on different 3D data representations
E Ahmed, A Saint, AER Shabayek… - arXiv preprint arXiv …, 2018 - arxiv.org
3D data is a valuable asset the computer vision filed as it provides rich information about the
full geometry of sensed objects and scenes. Recently, with the availability of both large 3D …
full geometry of sensed objects and scenes. Recently, with the availability of both large 3D …
Meshwalker: Deep mesh understanding by random walks
A Lahav, A Tal - ACM Transactions on Graphics (TOG), 2020 - dl.acm.org
Most attempts to represent 3D shapes for deep learning have focused on volumetric grids,
multi-view images and point clouds. In this paper we look at the most popular representation …
multi-view images and point clouds. In this paper we look at the most popular representation …
Multiview generative adversarial network and its application in pearl classification
This paper focuses on automatic pearl classification by adopting deep learning method,
using multiview pearl images. Traditionally, in order to get a satisfying classification result …
using multiview pearl images. Traditionally, in order to get a satisfying classification result …
Multi-view saliency guided deep neural network for 3-D object retrieval and classification
In this paper, we propose the multi-view saliency guided deep neural network (MVSG-DNN)
for 3D object retrieval and classification. This method mainly consists of three key modules …
for 3D object retrieval and classification. This method mainly consists of three key modules …
Rotationnet for joint object categorization and unsupervised pose estimation from multi-view images
A Kanezaki, Y Matsushita… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
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 …
takes multi-view images of an object as input and jointly estimates its pose and object …
Orthographicnet: A deep transfer learning approach for 3-d object recognition in open-ended domains
SH Kasaei - IEEE/ASME Transactions on Mechatronics, 2020 - ieeexplore.ieee.org
Nowadays, service robots are appearing more and more in our daily life. For this type of
robot, open-ended object category learning and recognition is necessary since no matter …
robot, open-ended object category learning and recognition is necessary since no matter …
General-purpose deep point cloud feature extractor
Depth sensors used in autonomous driving and gaming systems often report back 3D point
clouds. The lack of structure from these sensors does not allow these systems to take …
clouds. The lack of structure from these sensors does not allow these systems to take …