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

Multi-object detection and pose estimation in 3D point clouds: A fast grid-based Bayesian Filter

2013 ◽  
Author(s):  
Rui Pimentel de Figueiredo ◽  
Plinio Moreno ◽  
Alexandre Bernardino ◽  
Jose Santos-Victor
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Point Clouds ◽  
Bayesian Filter ◽  
3D Point Clouds ◽  
Grid Based

Workspace Modeling: Visualization and Pose Estimation of Teleoperated Construction Equipment from Point Clouds

2020 ◽  
Author(s):  
Jing Dao Chen ◽  
Pileun Kim ◽  
Dong-Ik Sun ◽  
Chang-Soo Han ◽  
Yong Han Ahn ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Point Clouds ◽  
Construction Equipment

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

scholarly journals Strong-Weak Feature Alignment for 3D Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1205
Author(s):  
Zhiyu Wang ◽  
Li Wang ◽  
Bin Dai
Keyword(s):  
Object Detection ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Feature Representation ◽  
Alignment Algorithm ◽  
3D Object ◽  
3D Point Clouds ◽  
Object Feature ◽  
3D Object Detection ◽  
Feature Alignment

Object detection in 3D point clouds is still a challenging task in autonomous driving. Due to the inherent occlusion and density changes of the point cloud, the data distribution of the same object will change dramatically. Especially, the incomplete data with sparsity or occlusion can not represent the complete characteristics of the object. In this paper, we proposed a novel strong–weak feature alignment algorithm between complete and incomplete objects for 3D object detection, which explores the correlations within the data. It is an end-to-end adaptive network that does not require additional data and can be easily applied to other object detection networks. Through a complete object feature extractor, we achieve a robust feature representation of the object. It serves as a guarding feature to help the incomplete object feature generator to generate effective features. The strong–weak feature alignment algorithm reduces the gap between different states of the same object and enhances the ability to represent the incomplete object. The proposed adaptation framework is validated on the KITTI object benchmark and gets about 6% improvement in detection average precision on 3D moderate difficulty compared to the basic model. The results show that our adaptation method improves the detection performance of incomplete 3D objects.

scholarly journals A Coarse-to-Fine Method for Estimating the Axis Pose Based on 3D Point Clouds in Robotic Cylindrical Shaft-in-Hole Assembly

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4064
Author(s):  
Can Li ◽  
Ping Chen ◽  
Xin Xu ◽  
Xinyu Wang ◽  
Aijun Yin
Keyword(s):  
Pose Estimation ◽  
Point Clouds ◽  
Geometric Constraints ◽  
Admittance Control ◽  
3D Vision ◽  
Axis Orientation ◽  
3D Point Clouds ◽  
Object Pose Estimation ◽  
Traditional Approaches ◽  
Coarse To Fine

In this work, we propose a novel coarse-to-fine method for object pose estimation coupled with admittance control to promote robotic shaft-in-hole assembly. Considering that traditional approaches to locate the hole by force sensing are time-consuming, we employ 3D vision to estimate the axis pose of the hole. Thus, robots can locate the target hole in both position and orientation and enable the shaft to move into the hole along the axis orientation. In our method, first, the raw point cloud of a hole is processed to acquire the keypoints. Then, a coarse axis is extracted according to the geometric constraints between the surface normals and axis. Lastly, axis refinement is performed on the coarse axis to achieve higher precision. Practical experiments verified the effectiveness of the axis pose estimation. The assembly strategy composed of axis pose estimation and admittance control was effectively applied to the robotic shaft-in-hole assembly.

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

Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhiyong Gao ◽  
Jianhong Xiang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
3D Object ◽  
3D Object Detection ◽  
Instance Segmentation

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

Adversarial Training on Point Clouds for Sim-to-Real 3D Object Detection

2021 ◽  
pp. 1-1
Author(s):  
Robert Debortoli ◽  
Fuxin Li ◽  
Ashish Kapoor ◽  
Geoffrey Hollinger
Keyword(s):  
Object Detection ◽  
Point Clouds ◽  
3D Object ◽  
Adversarial Training ◽  
3D Object Detection
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