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Semantic segmentation-assisted instance feature fusion for multi-level 3d part instance segmentation

Computational Visual Media 2023年第4期9卷 699-715页

作者： Chun-Yu Sun Xin Tong Yang Liu Institute for Advanced Study Tsinghua UniversityBeijing 100084China Microsoft Research Asia Beijing 100080China

Recognizing 3d part instances from a 3d point cloud is crucial for 3d structure and scene understanding.Several learning-based approaches use semantic segmentation and instance center prediction as training tasks and fail to further exploit the inherent relationship between shape semantics and part instances.In this paper,we present a new method for 3d part instance segmentation.Our method exploits semantic segmentation to fuse nonlocal instance features,such as center prediction,and further enhances the fusion scheme in a multi-and cross-level way.We also propose a semantic region center prediction task to train and leverage the prediction results to improve the clustering of instance points.Our method outperforms existing methods with a large-margin improvement in the PartNet benchmark.We also demonstrate that our feature fusion scheme can be applied to other existing methods to improve their performance in indoor scene instance segmentation tasks.

关键词： 3d part instance segmentation feature fusion 3d deep learning

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3d convolutional selective autoencoder for instability detection in combustion systems

能源与人工智能(英文)

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能源与人工智能(英文) 2021年第2期4卷 80-90页

作者： Tryambak Gangopadhyay Vikram Ramanan Adedotun Akintayo Paige K Boor Soumalya Sarkar Satyanarayanan R Chakravarthy Soumik Sarkar department of Mechanical Engineering Iowa State UniversityAmesIAUSA Indian Institute of Technology Madras ChennaiIndia Intel Corporation FolsomCAUSA Raytheon Technologies East HartfordCTUSA

While analytical solutions of critical(phase)transitions in dynamical systems are abundant for simple nonlinear systems,such analysis remains intractable for real-life dynamical systems.A key example is thermoacoustic insta-bility in combustion,where prediction or early detection of the onset of instability is a hard technical challenge,which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries.The instabilities arising in combustion chambers of engines are mathematically too complex to model.To address this issue in a data-driven manner instead,we propose a novel deep learning architecture called 3d convolutional selective autoencoder(3d-CSAE)to detect the evolution of self-excited oscillations using spatiotemporal data,i.e.,hi-speed videos taken from a swirl-stabilized combustor(laboratory surrogate of gas turbine engine combustor).3d-CSAE consists of filters to learn,in a hierarchical fashion,the complex visual and dynamic features related to combustion instability from the training videos(i.e.,two spatial dimensions for the image frames and the third dimension for time).We train the 3d-CSAE on frames of videos obtained from a limited set of operating conditions.We select the 3d-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video.The proposed model clearly shows performance improvement in detecting the precursors and the onset of instability.The machine learning-driven results are verified with physics-based off-line measures.Advanced active control mechanisms can directly leverage the proposed online detection capability of 3d-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions.

关键词： 3d deep learning Convolutional autoencoder Hi-speed video analytics Combustion instability Gas turbine engines Early detection Instability precursors Physics-based validation

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