scholarly journals General 3D modelling of novel objects from a single view

2010 ◽  
Author(s):  
Z Marton ◽  
D Pangercic ◽  
N Blodow ◽  
J Kleinehellefort ◽  
M Beetz
Keyword(s):  
3D Modelling ◽  
Single View ◽  
Novel Objects

scholarly journals One-shot learning and generation of dexterous grasps for novel objects

2015 ◽  
Vol 35 (8) ◽  
pp. 959-976 ◽  
Author(s):  
Marek Kopicki ◽  
Renaud Detry ◽  
Maxime Adjigble ◽  
Rustam Stolkin ◽  
Ales Leonardis ◽  
...  
Keyword(s):  
Incomplete Data ◽  
Point Cloud ◽  
Configuration Model ◽  
Training Set ◽  
Single View ◽  
First Choice ◽  
Novel Objects ◽  
Unseen Objects ◽  
Grasp Type ◽  
Unfamiliar Object

This paper presents a method for one-shot learning of dexterous grasps and grasp generation for novel objects. A model of each grasp type is learned from a single kinesthetic demonstration and several types are taught. These models are used to select and generate grasps for unfamiliar objects. Both the learning and generation stages use an incomplete point cloud from a depth camera, so no prior model of an object shape is used. The learned model is a product of experts, in which experts are of two types. The first type is a contact model and is a density over the pose of a single hand link relative to the local object surface. The second type is the hand-configuration model and is a density over the whole-hand configuration. Grasp generation for an unfamiliar object optimizes the product of these two model types, generating thousands of grasp candidates in under 30 seconds. The method is robust to incomplete data at both training and testing stages. When several grasp types are considered the method selects the highest-likelihood grasp across all the types. In an experiment, the training set consisted of five different grasps and the test set of 45 previously unseen objects. The success rate of the first-choice grasp is 84.4% or 77.7% if seven views or a single view of the test object are taken, respectively.

scholarly journals Learning better generative models for dexterous, single-view grasping of novel objects

2019 ◽  
Vol 38 (10-11) ◽  
pp. 1246-1267 ◽  
Author(s):  
Marek S Kopicki ◽  
Dominik Belter ◽  
Jeremy L Wyatt
Keyword(s):  
Generative Models ◽  
Test Set ◽  
Continual Improvement ◽  
Single View ◽  
Transfer Success ◽  
Novel Objects ◽  
Real Objects ◽  
Training Examples ◽  
Efficient Learning ◽  
Made In

This paper concerns the problem of how to learn to grasp dexterously, so as to be able to then grasp novel objects seen only from a single viewpoint. Recently, progress has been made in data-efficient learning of generative grasp models that transfer well to novel objects. These generative grasp models are learned from demonstration (LfD). One weakness is that, as this paper shall show, grasp transfer under challenging single-view conditions is unreliable. Second, the number of generative model elements increases linearly in the number of training examples. This, in turn, limits the potential of these generative models for generalization and continual improvement. In this paper, it is shown how to address these problems. Several technical contributions are made: (i) a view-based model of a grasp; (ii) a method for combining and compressing multiple grasp models; (iii) a new way of evaluating contacts that is used both to generate and to score grasps. Together, these improve grasp performance and reduce the number of models learned. These advances, in turn, allow the introduction of autonomous training, in which the robot learns from self-generated grasps. Evaluation on a challenging test set shows that, with innovations (i)–(iii) deployed, grasp transfer success increases from 55.1% to 81.6%. By adding autonomous training this rises to 87.8%. These differences are statistically significant. In total, across all experiments, 539 test grasps were executed on real objects.

HIGH-RAISED BUILDINGS IN URBAN AREA CONTRIBUTES TO THE SNATCH THEFT INCIDENTS

2017 ◽  
Vol 3 (2) ◽  
pp. 65
Author(s):  
Anith Nabilah Mustafa ◽  
Siti Rasidah Md Sakip
Keyword(s):  
Information System ◽  
Geographical Information System ◽  
Urban Area ◽  
Statistical Data ◽  
3D Modelling ◽  
Geographical Information ◽  
Residential Areas ◽  
Development Area ◽  
Crime Reports ◽  
Index Crime

Snatch theft incidents are on the rise in Malaysia especially in the cities. It tends to happen in cities because there is a possibility to commit it and both crime and urban are often associated with each other. The aim of this paper is to find out whether the building with two or more levels in urban area contributes to the snatch theft incidents. The present study has been taken up to detect the hotspots of snatch theft in Selangor, Malaysia. The crime data were obtained by requesting and analysing the index crime statistical data from the Royal Malaysian Police (RMP). This study made use of the Geographical Information System (GIS) where its 3D modelling function to construct, assess and analyse the area with high snatch theft cases. The crime reports of 2010 until 2015 were geocoded and the crime maps were prepared in ArcGIS 10.2. It was found that the hotspot area is a mix-used development area which consists mainly of commercial and residential areas of more than two-level buildings. 

Saturation Assessment in Law Resistivity Pay LRP 1D & 3D Modelling DH-Libya

2020 ◽  
Author(s):  
Walid Ablil ◽  
Abubakr Elsadawi ◽  
Abdulnasser Ownis
Keyword(s):  
3D Modelling

Quantifying idiosyncratic and shared contributions to stimulus evaluations

2018 ◽  
Author(s):  
Joel Eduardo Martinez ◽  
Friederike Funk ◽  
Alexander Todorov
Keyword(s):  
Repeated Measures ◽  
Variance Component ◽  
Psychological Problem ◽  
Psychological Research ◽  
Large Variability ◽  
Four Dimensions ◽  
Stimulus Evaluation ◽  
Crossed Random Effects ◽  
Novel Objects ◽  
Variance Component Estimates

A fundamental psychological problem is identifying the idiosyncratic and shared contributions to stimulus evaluation. However, there is no established method for estimating these contributions and the existing methods have led to divergent estimates. Moreover, in many studies participants rate the stimuli only once, although at least two measurements are required to estimate idiosyncratic contributions. Here, participants rated faces or novel objects on four dimensions (beautiful, approachable, likeable, dangerous) for a total of ten blocks to better estimate the preferences of individual raters. First, we show that both intra-rater and inter-rater agreement – measures related to idiosyncratic and shared contributions, respectively – increase with repeated measures. Second, to find best practices, we compared estimates from correlation indices and variance component approaches on stimulus-generality, evaluation-generality, data preprocessing steps, and sensitivity to measurement error (a largely ignored issue). The correlation indices changed monotonically and nonlinearly with more repeated measures. Variance component analyses showed large variability in estimates from only two repeated measures, but stabilized with more measures. While there was general agreement among approaches, the correlation approach was problematic for certain stimulus types and evaluation dimensions. Our results suggest that variance component estimates are more reliable as long as one collects more than two repeated measures, which is not the current norm in psychological research, and can be implemented using mixed models with crossed random effects. Recommendations for analysis and interpretations are provided.

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