Detecting 6D Poses of Target Objects From Cluttered Scenes by Learning to Align the Point Cloud Patches With the CAD Models
6D target object detection is of great importance to many applications such as robotics, industrial automation, and unmanned vehicles and is increasingly influencing broad industries including manufacturing, transportation, and retail industries, to name a few. This paper focuses on detecting the 6D...
Main Authors: | Xuzhan Chen, Youping Chen, Bang You, Jingming Xie, Homayoun Najjaran |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9241764/ |
Similar Items
-
A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color
by: Yajun Xu, et al.
Published: (2020-01-01) -
Point Cloud Instance Segmentation of Indoor Scenes Using Learned Pairwise Patch Relations
by: Lijie Yu, et al.
Published: (2021-01-01) -
View suggestion for interactive segmentation of indoor scenes
by: Sheng Yang, et al.
Published: (2017-03-01) -
Large Common Plansets-4-Points Congruent Sets for Point Cloud Registration
by: Cedrique Fotsing, et al.
Published: (2020-10-01) -
Applications of Graph Convolutional Networks and DeepGCNs in Point Cloud Part Segmentation and Upsampling
by: Abualshour, Abdulellah
Published: (2020)