PVN3D: A Deep Point-Wise 3D Keypoints Voting Network for 6DoF Pose Estimation
Yisheng He,Wei Sun,Haibin Huang,Jianran Liu,Haoqiang Fan,Jian Sun +5 more
- pp 11632-11641
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TLDR
PVN3D as mentioned in this paper proposes a deep Hough voting network to detect 3D keypoints of objects and then estimate the 6D pose parameters within a least-squares fitting manner.Abstract:
In this work, we present a novel data-driven method for robust 6DoF object pose estimation from a single RGBD image. Unlike previous methods that directly regressing pose parameters, we tackle this challenging task with a keypoint-based approach. Specifically, we propose a deep Hough voting network to detect 3D keypoints of objects and then estimate the 6D pose parameters within a least-squares fitting manner. Our method is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation. It allows us to fully utilize the geometric constraint of rigid objects with the extra depth information and is easy for a network to learn and optimize. Extensive experiments were conducted to demonstrate the effectiveness of 3D-keypoint detection in the 6D pose estimation task. Experimental results also show our method outperforms the state-of-the-art methods by large margins on several benchmarks. Code and video are available at https://github.com/ethnhe/PVN3D.git.read more
Citations
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Proceedings ArticleDOI
FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
TL;DR: FFB6D as discussed by the authors proposes a bidirectional fusion network to combine appearance and geometry information for representation learning as well as output representation selection, which can leverage local and global complementary in-formation from the other one to obtain better representations.
Journal ArticleDOI
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review
TL;DR: Three key tasks during vision-based robotic grasping are concluded, which are object localization, object pose estimation and grasp estimation, which include 2D planar grasp methods and 6DoF grasp methods.
Proceedings ArticleDOI
FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism
TL;DR: Li et al. as discussed by the authors proposed a fast shape-based network (FS-Net) with efficient category-level feature extraction for 6D pose estimation from a monocular RGB-D image.
Book ChapterDOI
Cascade Graph Neural Networks for RGB-D Salient Object Detection
TL;DR: Cascade Graph Neural Networks (Cas-GNN) as mentioned in this paper is a unified framework which is capable of comprehensively distilling and reasoning the mutual benefits between these two data sources through a set of cascade graphs, to learn powerful representations for RGB-D salient object detection.
Proceedings ArticleDOI
RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images
TL;DR: RGBD-Grasp as discussed by the authors decouples the grasp detection into two sub-tasks where RGB and depth information are processed separately, and achieves state-of-the-art results on GraspNet-1Billion dataset compared with several baselines.
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