Pointvotenet: Accurate Object Detection And 6 DOF Pose Estimation In Point Clouds

Hagelskjar, Frederik; Buch, Anders Glent

doi:10.1109/icip40778.2020.9191119

Cited by 29 publications

(27 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PointVoteNet [ 30 ] supports both global and local features, as it is based on PointNet by Qi et al [ 50 ], which represents target objects as a cascade of global and local features.…”

Section: Methodsmentioning

confidence: 99%

“…The coordinate-based disentagled pose network (CDPN) by Li et al [ 29 ] predicts translation and rotation, separately. PointVoteNet by Hagelskjar and Buch [ 30 ], unlike most other neural-network-based methods, estimates poses from unordered point clouds. CosyPose by Labbé et al [ 5 ] also supports multi-view pose estimation and was one of the top performers in the BOP Challenge 2020 [ 3 ].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of 6D Object Detection Based on 3D Models for Industrial Applications

2022

View full text Add to dashboard Cite

Six-dimensional object detection of rigid objects is a problem especially relevant for quality control and robotic manipulation in industrial contexts. This work is a survey of the state of the art of 6D object detection with these use cases in mind, specifically focusing on algorithms trained only with 3D models or renderings thereof. Our first contribution is a listing of requirements typically encountered in industrial applications. The second contribution is a collection of quantitative evaluation results for several different 6D object detection methods trained with synthetic data and the comparison and analysis thereof. We identify the top methods for individual requirements that industrial applications have for object detectors, but find that a lack of comparable data prevents large-scale comparison over multiple aspects.

show abstract

“…PointVoteNet [ 30 ] supports both global and local features, as it is based on PointNet by Qi et al [ 50 ], which represents target objects as a cascade of global and local features.…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Survey of 6D Object Detection Based on 3D Models for Industrial Applications

2022

View full text Add to dashboard Cite

show abstract

“…The PointPoseNet classifier for 6DoF objects gives the idea of inference of rigid objects using deep learning in point clouds. A point-to-point correspondence assignment is performed with a joint classification and segmentation within 1060 a point cloud system [82]. Capellen [26] suggested that ConvPoseCNN has evolved from the concept of PoseCNN but can avoid cutting individual objects.…”

Section: A 6d Pose Estimation Directly From Rgb Imagesmentioning

confidence: 99%

“…A number of 3D point cloud networks can be replaced directly by the PointNet network [82] for potential improvement in accurate 3D object detection and 6DF pose estimation. A computational budget can be created to know the appropriate time for the softer version of the PoseAgent [125] classification.…”

Section: ) Improving the 3d Point Cloud Networkmentioning

confidence: 99%

A Comprehensive Review on 3D Object Detection and 6D Pose Estimation With Deep Learning

Hoque

Arafat

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of freedom) pose assumptions are widely discussed and studied in the field. In the 3D object detection process, classifications are centered on the object's size, position, and direction. And in 6D pose assumptions, networks emphasize 3D translation and rotation vectors. Successful application of these strategies can have a huge impact on various machine learning-based applications, including the autonomous vehicles, the robotics industry, and the augmented reality sector. Although extensive work has been done on 3D object detection with a pose assumption from RGB images, the challenges have not been fully resolved. Our analysis provides a comprehensive review of the proposed contemporary techniques for complete 3D object detection and the recovery of 6D pose assumptions of an object. In this review research paper, we have discussed several proposed sophisticated methods in 3D object detection and 6D pose estimation, including some popular data sets, evaluation matrix, and proposed method challenges. Most importantly, this study makes an effort to offer some possible future directions in 3D object detection and 6D pose estimation. We accept the autonomous vehicle as the sample case for this detailed review. Finally, this review provides a complete overview of the latest in-depth learning-based research studies related to 3D object detection and 6D pose estimation systems and also points out a comparison between some popular frameworks. To be more concise, we propose a detailed summary of the state-of-the-art techniques of modern deep learningbased object detection and pose estimation models.

show abstract

“…We consider three datasets, the LineMOD dataset (LM) [9], LineMOD Occlusion (LMO) [32] and YCB video dataset (YCBV) [4]. Depending on the applicability of the methods, for each dataset, we compare to a subset of stateof-the-art methods SSD-6D [29], EEPG-AAE [15], Cloud-Pose [8], PVNet [30], PoseCNN [4], DenseFusion [12], PVN3D [13] and PointVoteNet [31].…”

Section: A Datasets and Experiments Setupmentioning

confidence: 99%

CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds

Gao

Lauri

et al. 2021

Preprint

View full text Add to dashboard Cite

It is often desired to train 6D pose estimation systems on synthetic data because manual annotation is expensive. However, due to the large domain gap between the synthetic and real images, synthesizing color images is expensive. In contrast, this domain gap is considerably smaller and easier to fill for depth information. In this work, we present a system that regresses 6D object pose from depth information represented by point clouds, and a lightweight data synthesis pipeline that creates synthetic point cloud segments for training. We use an augmented autoencoder (AAE) for learning a latent code that encodes 6D object pose information for pose regression. The data synthesis pipeline only requires texture-less 3D object models and desired viewpoints, and it is cheap in terms of both time and hardware storage. Our data synthesis process is up to three orders of magnitude faster than commonly applied approaches that render RGB image data. We show the effectiveness of our system on the LineMOD, LineMOD Occlusion, and YCB Video datasets. The implementation of our system is available at: https://github.com/GeeeG/CloudAAE.

show abstract

Pointvotenet: Accurate Object Detection And 6 DOF Pose Estimation In Point Clouds

Cited by 29 publications

References 14 publications

A Survey of 6D Object Detection Based on 3D Models for Industrial Applications

A Survey of 6D Object Detection Based on 3D Models for Industrial Applications

A Comprehensive Review on 3D Object Detection and 6D Pose Estimation With Deep Learning

CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds

Contact Info

Product

Resources

About