scholarly journals Viewpoint-aware object detection and continuous pose estimation

2012 ◽  
Vol 30 (12) ◽  
pp. 923-933 ◽  
Author(s):  
Daniel Glasner ◽  
Meirav Galun ◽  
Sharon Alpert ◽  
Ronen Basri ◽  
Gregory Shakhnarovich
Keyword(s):  
Object Detection ◽  
Pose Estimation

scholarly journals Object Detection And Autoencoder-Based 6d Pose Estimation For Highly Cluttered Bin Picking

2021 ◽  
Author(s):  
Timon Hofer ◽  
Faranak Shamsafar ◽  
Nuri Benbarka ◽  
Andreas Zell
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Bin Picking

DIB: Piled Man-made Object Detection and Pose Estimation in Point Cloud Blocks

2021 ◽  
Author(s):  
Weiqian Guo ◽  
Rendong Ying ◽  
Peilin Liu ◽  
Weihang Wang
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Point Cloud

Enhanced Gaze Following via Object Detection and Human Pose Estimation

2019 ◽  
pp. 502-513 ◽  
Author(s):  
Jian Guan ◽  
Liming Yin ◽  
Jianguo Sun ◽  
Shuhan Qi ◽  
Xuan Wang ◽  
...  
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Gaze Following ◽  
Human Pose Estimation ◽  
Human Pose

Object Detection and Pose Estimation Approaches for Soldering Danger Detection

2021 ◽  
Author(s):  
Tomoya Yasunaga ◽  
Tetsuya Oda ◽  
Nobuki Saito ◽  
Aoto Hirata ◽  
Kyohei Toyoshima ◽  
...  
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Danger Detection

scholarly journals Feasibility Study of Textureless Object Detection and Pose Estimation Based on a Model with 3D Edgels and Surfaces

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Kimitoshi Yamazaki ◽  
Kiyohiro Sogen ◽  
Takashi Yamamoto ◽  
Masayuki Inaba
Keyword(s):  
Object Detection ◽  
Feasibility Study ◽  
Pose Estimation ◽  
Template Matching ◽  
Input Data ◽  
Three Dimensional ◽  
Target Object ◽  
Textural Features ◽  
Two Dimensional ◽  
Home Appliances

Abstract This paper describes a method for the detection of textureless objects. Our target objects include furniture and home appliances, which have no rich textural features or characteristic shapes. Focusing on the ease of application, we define a model that represents objects in terms of three-dimensional edgels and surfaces. Object detection is performed by superimposing input data on the model. A two-stage algorithm is applied to bring out object poses. Surfaces are used to extract candidates fromthe input data, and edgels are then used to identify the pose of a target object using two-dimensional template matching. Experiments using four real furniture and home appliances were performed to show the feasibility of the proposed method.We suggest the possible applicability in occlusion and clutter conditions.

An Integrated Framework for 3-D Modeling, Object Detection, and Pose Estimation From Point-Clouds

2015 ◽  
Vol 64 (3) ◽  
pp. 683-693 ◽  
Author(s):  
Yulan Guo ◽  
Mohammed Bennamoun ◽  
Ferdous Sohel ◽  
Min Lu ◽  
Jianwei Wan
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Point Clouds ◽  
Integrated Framework
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