Pseudo-Siamese Graph Matching Network for Textureless Objects’ 6-D Pose Estimation

Wu, Chenrui; Чэн, Лонг; He, Zaixing; Jiang, Junjie

doi:10.1109/tie.2021.3070501

Cited by 27 publications

(14 citation statements)

References 52 publications

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“…(2) Calculate the poses for all the target objects. Most of the existing works [9,25,26,32] solve the two problems in a unified framework for boosting the performance on commonly used open-evaluation datasets such as LINEMOD, Occlusion LINEMOD, and YCB Video. However, all of these datasets only contain a single target for each of the classes in one frame.…”

Section: Methodsmentioning

confidence: 99%

“…where S k denotes the similarity matrix for object k. In [26], the softmax cross-entropy losswhich is the most generally used loss function for traditional classification problem-was chosen to select the corresponding node from 3D model for each image pixel that belongs to the target object. The lost function can be described as…”

Section: Masked Circle Loss For Matching Dense Correspondencesmentioning

confidence: 99%

“…Recently, finding dense correspondences using the deep neural networks has shown advantages in 6D pose estimation [25,26]. The per-pixel matching scheme was utilized to design and train the network.…”

Section: Introductionmentioning

confidence: 99%

“…The per-pixel matching scheme was utilized to design and train the network. In [26], a pseudo-siamese matching network was proposed to match dense correspondences in high-dimension; then, the dense correspondences were used to calculate the target pose through Perspective-n-Points (PnP) method [27]. This method achieved state-of-the-art performance in LineMod [28] and Occlusion-LineMod [29] datasets.…”

Section: Introductionmentioning

confidence: 99%

“…(2) We introduce the circle loss, a well-known loss function in metric learning, to measure the similarities between pixelwise deep features from a 2D image and nodewise deep features from a 3D mesh model. We analyze the reason why the softmax cross-entropy loss [31] used in [26] is not suitable for dense correspondences matching and compare the proposed masked circle loss with the softmax cross-entropy loss though ablation studies to show the superiority of the proposed loss.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects’ 6D Pose Estimation

Chen

2021

Applied Sciences

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6D pose estimation of objects is essential for intelligent manufacturing. Current methods mainly place emphasis on the single object’s pose estimation, which limit its use in real-world applications. In this paper, we propose a multi-instance framework of 6D pose estimation for textureless objects in an industrial environment. We use a two-stage pipeline for this purpose. In the detection stage, EfficientDet is used to detect target instances from the image. In the pose estimation stage, the cropped images are first interpolated into a fixed size, then fed into a pseudo-siamese graph matching network to calculate dense point correspondences. A modified circle loss is defined to measure the differences of positive and negative correspondences. Experiments on the antenna support demonstrate the effectiveness and advantages of our proposed method.

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Section: Methodsmentioning

confidence: 99%

Section: Masked Circle Loss For Matching Dense Correspondencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects’ 6D Pose Estimation

Chen

2021

Applied Sciences

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Object pose estimation based on stereo vision with improvedK‐DtreeICPalgorithm

Huang

Wang

Yun

et al. 2023

Concurrency and Computation

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SummaryWith the wide application of stereovision in SLAM, object pose estimation has gradually become one of the research hotspots. This article proposes an object pose estimation for robotic grasping based on stereo vision with improved K‐D tree ICP algorithm. The feature points and feature descriptors of the point cloud of the object to be captured are extracted, and the feature template set is established. The SAC‐IA algorithm is used to carry out initial registration of the point cloud of the target, and the ICP algorithm based on K‐D tree is used for fine registration. The experimental results show that the average coincidence degree of the final registration of the proposed object pose estimation method reaches 94.1%, and the accurate 6D pose of the object to be grasped is obtained.

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RT-less: a multi-scene RGB dataset for 6D pose estimation of reflective texture-less objects

Zhao,

Li,

Chao

et al. 2023

Vis Comput

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Pseudo-Siamese Graph Matching Network for Textureless Objects’ 6-D Pose Estimation

Cited by 27 publications

References 52 publications

A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects’ 6D Pose Estimation

A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects’ 6D Pose Estimation

Object pose estimation based on stereo vision with improvedK‐DtreeICPalgorithm

RT-less: a multi-scene RGB dataset for 6D pose estimation of reflective texture-less objects

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