MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion

Wada, Kazumi; Sucar, Edgar; James, Stephen; Lenton, Daniel; Davison, Andrew J.

doi:10.1109/cvpr42600.2020.01455

Cited by 90 publications

(41 citation statements)

References 27 publications

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“…Then, Wang et al [74] propose DenseFusion, which fully leverages color and depth information, leading to robustness in heavy occlusion and changing lighting conditions. Furthermore, MoreFusion [75] performs pose prediction with surrounding spatial awareness, and joint multiple-object pose optimization, which greatly promotes consistency and accuracy of pose estimation in cluttered scenes with heavy occlusion and contracting objects. Second, besides handling complex scenarios, recent studies have shown interest in designing more lightweight network architectures to prompt real-time performance.…”

Section: (Rgb)d-based Methodsmentioning

confidence: 99%

Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview

Fan¹,

Zhu²,

He³

et al. 2021

Preprint

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Object pose detection and tracking has recently attracted increasing attention due to its wide applications in many areas, such as autonomous driving, robotics, and augmented reality. Among methods for object pose detection and tracking, deep learning is the most promising one that has shown better performance than others. However, there is lack of survey study about latest development of deep learning based methods. Therefore, this paper presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route. To achieve a more thorough introduction, the scope of this paper is limited to methods taking monocular RGB/RGBD data as input, covering three kinds of major tasks: instance-level monocular object pose detection, category-level monocular object pose detection, and monocular object pose tracking. In our work, metrics, datasets, and methods about both detection and tracking are presented in detail. Comparative results of current state-of-the-art methods on several publicly available datasets are also presented, together with insightful observations and inspiring future research directions.

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Section: (Rgb)d-based Methodsmentioning

confidence: 99%

Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview

Fan¹,

Zhu²,

He³

et al. 2021

Preprint

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“…Another research line for completing the perceived geometry and estimating its pose is via multi-view fusion [12]. This kind of methods is able to alleviate the damping factors of perception such as poor lighting conditions, clutter, and occlusions.…”

Section: Object-oriented Methodsmentioning

confidence: 99%

GraspVDN: scene-oriented grasp estimation by learning vector representations of grasps

Dong

Tian

Bao

et al. 2021

Complex Intell. Syst.

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Grasp estimation is a fundamental technique crucial for robot manipulation tasks. In this work, we present a scene-oriented grasp estimation scheme taking constraints of the grasp pose imposed by the environment into consideration and training on samples satisfying the constraints. We formulate valid grasps for a parallel-jaw gripper as vectors in a two-dimensional (2D) image and detect them with a fully convolutional network that simultaneously estimates the vectors’ origins and directions. The detected vectors are then converted to 6 degree-of-freedom (6-DOF) grasps with a tailored strategy. As such, the network is able to detect multiple grasp candidates from a cluttered scene in one shot using only an RGB image as input. We evaluate our approach on the GraspNet-1Billion dataset and archived comparable performance as state-of-the-art while being efficient in runtime.

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“…Early works [10,11] directly apply 2D CNN on RGB-D image for 6DoF pose regression, while 2D CNN does not characterize 3D geometric information well. To better explore the 3D geometric information, the 3D space corresponding to depth image is divided into voxel grids, and then 3D CNN is applied on voxels [18][19][20]. Higher-dimensional convolution on 3D voxel requires huge computational resources.…”

Section: Related Workmentioning

confidence: 99%

6DoF Pose Estimation of Transparent Object from a Single RGB-D Image

Chen

Yao

et al. 2020

Sensors

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6DoF object pose estimation is a foundation for many important applications, such as robotic grasping, automatic driving, and so on. However, it is very challenging to estimate 6DoF pose of transparent object which is commonly seen in our daily life, because the optical characteristics of transparent material lead to significant depth error which results in false estimation. To solve this problem, a two-stage approach is proposed to estimate 6DoF pose of transparent object from a single RGB-D image. In the first stage, the influence of the depth error is eliminated by transparent segmentation, surface normal recovering, and RANSAC plane estimation. In the second stage, an extended point-cloud representation is presented to accurately and efficiently estimate object pose. As far as we know, it is the first deep learning based approach which focuses on 6DoF pose estimation of transparent objects from a single RGB-D image. Experimental results show that the proposed approach can effectively estimate 6DoF pose of transparent object, and it out-performs the state-of-the-art baselines by a large margin.

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MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion

Cited by 90 publications

References 27 publications

Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview

Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview

GraspVDN: scene-oriented grasp estimation by learning vector representations of grasps

6DoF Pose Estimation of Transparent Object from a Single RGB-D Image

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