Weiquan Liu
Intel
37 Papers
105 Citations
Weiquan Liu is an academic researcher from Intel. The author has contributed to research in topics: Computer science & Point cloud. The author has an hindex of 3, co-authored 6 publications.
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Papers
Patent
Method and apparatus for concept-based searching across a network
Joe F. Zhou,Weiquan Liu +1 more
- 23 Aug 2000
TL;DR: In this paper, a local database is searched for data terms that match the search term and the related terms and the data terms are from documents residing on websites located on servers across a network.
53
3D building model generation from MLS point cloud and 3D mesh using multi-source data fusion
Weiquan Liu,Yu Zang,Zhangyue Xiong,Xuesheng Bian,Chenglu Wen,Xiaoyun Lu,Cheng Wang,Jose Marcato,Wesley Nunes Gonçalves,Jonathan Li +9 more
TL;DR: In this paper , a multi-source 3D data quality evaluation network (MS3DQE-Net) is proposed for evaluating the quality of 3D meshes and 3D point clouds.
37
Patent
Method and apparatus for summarizing multiple documents using a subsumption model
Weiquan Liu,Joe F. Zhou +1 more
- 07 Sep 2000
TL;DR: A method and apparatus for parsing a plurality of documents, selecting paragraphs from the documents through subsuming relation calculation, and rewriting the selected paragraphs into a summary is described in this article.
24
Patent
Method and apparatus for determining text passage similarity
Weiquan Liu,Joe F. Zhou +1 more
- 30 Sep 2000
TL;DR: In this paper, a method for classifying noun phrases in a first text passage and a second text passage into a number of classifications was proposed. But the method was based on the similarity between noun phrases of the same classification.
23
Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing: A survey
Jibril Muhammad Adam,Weiquan Liu,Yu Zang,M. Kamran Afzal,Saifullahi Aminu Bello,Abdullahi Uwaisu Muhammad,Sheng Wang,Jonathan Li +7 more
TL;DR: In this article , a survey of recent advances in utilizing deep learning techniques for semantic segmentation of urban-scale 3D meshes is presented, where different approaches employed by mesh-based learning methods to generalize and implement learning techniques on the mesh surface, and then describe how the element-wise classification tasks are achieved through these methods.
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