Outline viewpoint feature histogram: An improved point cloud descriptor for recognition and grasping of workpieces

Ru, Changlei; Wang, Fei; Li, Tong; Ren, Baiming; Yan, Xin

doi:10.1063/5.0023164

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…Some techniques use such algorithms to improve over previous measures [SLF*11], or to assign scores to candidate views or photographs [KTL*17, YLLY19, ZFY20]. In the context of point cloud recognition tasks, viewpoint feature histograms have been proposed, which are used as feature descriptors [RBTH10, RWL*21], which can be used for point cloud registration [AMB17]. In the work of Fang et al .…”

Section: Related Workmentioning

confidence: 99%

Learning Human Viewpoint Preferences from Sparsely Annotated Models

Hartwig

Schelling

Onzenoodt

et al. 2022

Computer Graphics Forum

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View quality measures compute scores for given views and are used to determine an optimal view in viewpoint selection tasks. Unfortunately, despite the wide adoption of these measures, they are rather based on computational quantities, such as entropy, than human preferences. To instead tailor viewpoint measures towards humans, view quality measures need to be able to capture human viewpoint preferences. Therefore, we introduce a large-scale crowdsourced data set, which contains 58k annotated viewpoints for 3220 ModelNet40 models. Based on this data, we derive a neural view quality measure abiding to human preferences. We further demonstrate that this view quality measure not only generalizes to models unseen during training, but also to unseen model categories. We are thus able to predict view qualities for single images, and directly predict human preferred viewpoints for 3D models by exploiting point-based learning technology, without requiring to generate intermediate images or sampling the view sphere. We will detail our data collection procedure, describe the data analysis and model training and will evaluate the predictive quality of our trained viewpoint measure on unseen models and categories. To our knowledge, this is the first deep learning approach to predict a view quality measure solely based on human preferences.

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Section: Related Workmentioning

confidence: 99%

Learning Human Viewpoint Preferences from Sparsely Annotated Models

Hartwig

Schelling

Onzenoodt

et al. 2022

Computer Graphics Forum

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Tracking moving target for 6 degree-of-freedom robot manipulator with adaptive visual servoing based on deep reinforcement learning PID controller

Wang

Ren

Liu

et al. 2022

Review of Scientific Instruments

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In this paper, an image-based visual servoing (IBVS) controller with a 6 degree-of-freedom robotic manipulator that tracks moving objects is investigated using the proposed Deep Q-Networks and proportional–integral–derivative (DQN-PID) controller. First, the classical IBVS controller and the problem of feature loss and large steady-state error for tracking moving targets are introduced. Then, a DQN-PID based IBVS method is proposed to solve the problem of feature loss and large steady-state error and improve the servo precision, as the existing methods are hard to use for solve the problems. Specifically, the IBVS method is inherited by our controller to build the tracking model, and a value-based reinforcement learning method is proposed as an adaptive law for dynamically tuning the PID parameters in the discrete space, which can track the moving target and keep the servo feature in the field of the camera. Finally, compared with the different existing methods, the DQN-PID based IBVS method has merits of higher accuracy and more stable tracking, or generalization.

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Recognition and robot grasping of disordered workpieces with 3D laser line profile sensor

Bin

Wang

et al. 2023

Systems Science & Control Engineering

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Outline viewpoint feature histogram: An improved point cloud descriptor for recognition and grasping of workpieces

Cited by 5 publications

References 20 publications

Learning Human Viewpoint Preferences from Sparsely Annotated Models

Learning Human Viewpoint Preferences from Sparsely Annotated Models

Tracking moving target for 6 degree-of-freedom robot manipulator with adaptive visual servoing based on deep reinforcement learning PID controller

Recognition and robot grasping of disordered workpieces with 3D laser line profile sensor

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