Furniture models learned from the WWW: using web catalogs to locate and categorize unknown furniture pieces in 3D laser scans

In this article, we investigate how autonomous robots can exploit the high quality information already available from the WWW concerning 3-D models of office furniture. Apart from the hobbyist effort in Google 3-D Warehouse, many companies providing office furnishings already have the models for considerable portions of the objects found in our workplaces and homes. In particular, we present an approach that allows a robot to learn generic models of typical office furniture using examples found in the Web. These generic models are then used by the robot to locate and categorize unknown furniture in real indoor environments

Development of a STEP-compliant design and manufacturing framework for discrete sheet metal bend parts

Metal sheets have the ability to be formed into nonstandard sizes and sections. Displacement-controlled computer numerical control press brakes are used for three-dimensional sheet metal forming. Although the subject of vendor neutral computer-aided technologies (computer-aided design, computer-aided process planning and computer-aided manufacturing) is widely researched for machined parts, research in the field of sheet metal parts is very sparse. Blank development from three-dimensional computer-aided design model depends on the bending tools geometry and metal sheet properties. Furthermore, generation and propagation of bending errors depend on individual bend sequences. Bend sequence planning is carried out to minimize bending errors, keeping in view the available tooling geometry and the sheet material properties’ variation. Research reported in this article attempts to develop a STEP-compliant, vendor neutral design and manufacturing framework for discrete sheet metal bend parts to provide a capability of bidirectional communication between design and manufacturing cycles. Proposed framework will facilitate the use of design information downstream at the manufacturing stage in the form of bending workplan, bending workingsteps and a feedback mechanism to the upstage product designer. In order to realize this vendor neutral framework, STEP (ISO 10303), AP203, AP207, and AP219 along with STEP-NC (ISO14649) have been used to provide a basis of vendor neutral data modeling.N/

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