Bi-stage multi-modal 3D instance segmentation method for production workshop scene

Tang, Zaizuo; Chen, Guangzhu; Han, Yinhe; Liao, Xiaojuan; Ru, Qingjun; Wu, Yuanyuan

doi:10.1016/j.engappai.2022.104858

Cited by 8 publications

(1 citation statement)

References 27 publications

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“…3D Instance Segmentation [40,42,44,46,45,48] is a pivotal task in the field of computer vision, with the primary goal of identifying and segmenting individual entities within 3D point cloud data. It is required to not only ascertain the category of each object, but also differentiate between distinct entities of the same class.…”

Section: Introductionmentioning

confidence: 99%

Potential development of dairy production in Sichuan Province, China

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Most existing 3D instance segmentation methods are derived from 3D semantic segmentation models. However, these indirect approaches suffer from certain limitations. They fail to fully leverage global and local semantic information for accurate prediction, which hampers the overall performance of the 3D instance segmentation framework. To address these issues, this paper presents PSGformer, a novel 3D instance segmentation network. PSGformer incorporates two key advancements to enhance the performance of 3D instance segmentation. Firstly, we propose a Multi-Level Semantic Aggregation Module, which effectively captures scene features by employing foreground point filtering and multi-radius aggregation. This module enables the acquisition of more detailed semantic information from global and local perspectives. Secondly, PSGformer introduces a Parallel Feature Fusion Transformer Module that independently processes super-point features and aggregated features using transformers. The model achieves a more comprehensive feature representation by the features which connect global and local features. We conducted extensive experiments on the ScanNetv2 dataset. Notably, PSGformer exceeds compared state-of-the-art methods by 2.2% on ScanNetv2 hidden test set in terms of mAP. Our code and models will be publicly released.

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Section: Introductionmentioning

confidence: 99%

Potential development of dairy production in Sichuan Province, China

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The use of CNNs in VR/AR/MR/XR: a systematic literature review

Cortes,

Bermejo,

Juiz

2024

Virtual Reality

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This study offers a systematic literature review on the application of Convolutional Neural Networks in Virtual Reality, Augmented Reality, Mixed Reality, and Extended Reality technologies. We categorise these applications into three primary classifications: interaction, where the networks amplify user engagements with virtual and augmented settings; creation, showcasing the networks’ ability to assist in producing high-quality visual representations; and execution, emphasising the optimisation and adaptability of apps across diverse devices and situations. This research serves as a comprehensive guide for academics, researchers, and professionals in immersive technologies, offering profound insights into the cross-disciplinary realm of network applications in these realities. Additionally, we underscore the notable contributions concerning these realities and their intersection with neural networks.

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