CN111062423B - 基于自适应特征融合的点云图神经网络的点云分类方法 - Google Patents
基于自适应特征融合的点云图神经网络的点云分类方法 Download PDFInfo
- Publication number
- CN111062423B CN111062423B CN201911201999.5A CN201911201999A CN111062423B CN 111062423 B CN111062423 B CN 111062423B CN 201911201999 A CN201911201999 A CN 201911201999A CN 111062423 B CN111062423 B CN 111062423B
- Authority
- CN
- China
- Prior art keywords
- feature
- point cloud
- features
- local
- adaptive
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 18
- 230000004927 fusion Effects 0.000 title claims abstract description 13
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 11
- 230000003044 adaptive effect Effects 0.000 claims abstract description 6
- 230000007246 mechanism Effects 0.000 claims abstract description 6
- 238000012545 processing Methods 0.000 claims description 12
- 238000012549 training Methods 0.000 claims description 11
- 230000006870 function Effects 0.000 claims description 10
- 230000009466 transformation Effects 0.000 claims description 7
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 230000001537 neural effect Effects 0.000 claims description 3
- 238000011176 pooling Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 230000000630 rising effect Effects 0.000 claims description 2
- 230000008034 disappearance Effects 0.000 abstract description 2
- 239000000284 extract Substances 0.000 abstract description 2
- 208000037170 Delayed Emergence from Anesthesia Diseases 0.000 abstract 1
- 238000013135 deep learning Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000001174 ascending effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Molecular Biology (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
Description
Claims (2)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201911201999.5A CN111062423B (zh) | 2019-11-29 | 2019-11-29 | 基于自适应特征融合的点云图神经网络的点云分类方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201911201999.5A CN111062423B (zh) | 2019-11-29 | 2019-11-29 | 基于自适应特征融合的点云图神经网络的点云分类方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN111062423A CN111062423A (zh) | 2020-04-24 |
CN111062423B true CN111062423B (zh) | 2022-04-26 |
Family
ID=70299656
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201911201999.5A Active CN111062423B (zh) | 2019-11-29 | 2019-11-29 | 基于自适应特征融合的点云图神经网络的点云分类方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111062423B (zh) |
Families Citing this family (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111626217B (zh) * | 2020-05-28 | 2023-08-22 | 宁波博登智能科技有限公司 | 一种基于二维图片和三维点云融合的目标检测和追踪方法 |
CN112633376A (zh) * | 2020-12-24 | 2021-04-09 | 南京信息工程大学 | 基于深度学习的点云数据地物分类方法、***与存储介质 |
CN112819080B (zh) * | 2021-02-05 | 2022-09-02 | 四川大学 | 一种高精度通用的三维点云识别方法 |
CN113052109A (zh) * | 2021-04-01 | 2021-06-29 | 西安建筑科技大学 | 一种3d目标检测***及其3d目标检测方法 |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108171217A (zh) * | 2018-01-29 | 2018-06-15 | 深圳市唯特视科技有限公司 | 一种基于点融合网络的三维物体检测方法 |
CN109523552A (zh) * | 2018-10-24 | 2019-03-26 | 青岛智能产业技术研究院 | 基于视锥点云的三维物体检测方法 |
-
2019
- 2019-11-29 CN CN201911201999.5A patent/CN111062423B/zh active Active
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108171217A (zh) * | 2018-01-29 | 2018-06-15 | 深圳市唯特视科技有限公司 | 一种基于点融合网络的三维物体检测方法 |
CN109523552A (zh) * | 2018-10-24 | 2019-03-26 | 青岛智能产业技术研究院 | 基于视锥点云的三维物体检测方法 |
Non-Patent Citations (1)
Title |
---|
StructureNet;Kaichun Mo等;《ACM Transactions on Graphics (TOG)》;20191108;全文 * |
Also Published As
Publication number | Publication date |
---|---|
CN111062423A (zh) | 2020-04-24 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111062423B (zh) | 基于自适应特征融合的点云图神经网络的点云分类方法 | |
CN107945204B (zh) | 一种基于生成对抗网络的像素级人像抠图方法 | |
CN107133943B (zh) | 一种防震锤缺陷检测的视觉检测方法 | |
CN108109162B (zh) | 一种利用自适应特征融合的多尺度目标跟踪方法 | |
CN113076994B (zh) | 一种开集域自适应图像分类方法及*** | |
Li et al. | Automatic bridge crack identification from concrete surface using ResNeXt with postprocessing | |
CN109743642B (zh) | 基于分层循环神经网络的视频摘要生成方法 | |
CN112818969A (zh) | 一种基于知识蒸馏的人脸姿态估计方法及*** | |
CN112288758B (zh) | 一种电力设备红外与可见光图像配准方法 | |
CN111368634B (zh) | 基于神经网络的人头检测方法、***及存储介质 | |
CN114972312A (zh) | 基于YOLOv4-Tiny改进的绝缘子缺陷检测方法 | |
Li et al. | A review of deep learning methods for pixel-level crack detection | |
CN116342894B (zh) | 基于改进YOLOv5的GIS红外特征识别***及方法 | |
CN112819063B (zh) | 一种基于改进的Focal损失函数的图像识别方法 | |
Zhou et al. | Defect detection method based on knowledge distillation | |
CN117152601A (zh) | 一种基于动态感知区域路由的水下目标检测方法及*** | |
CN116030292A (zh) | 基于改进ResNext的混凝土表面粗糙度检测方法 | |
CN115049842B (zh) | 一种飞机蒙皮图像损伤检测与2d-3d定位方法 | |
CN114067155B (zh) | 基于元学习的图像分类方法、装置、产品及存储介质 | |
CN116051808A (zh) | 一种基于YOLOv5的轻量化零件识别定位方法 | |
Pang et al. | Target tracking based on siamese convolution neural networks | |
Liu et al. | Tiny electronic component detection based on deep learning | |
CN116861261B (zh) | 自动驾驶模型的训练方法、部署方法、***、介质和设备 | |
CN117809169B (zh) | 一种小样本水下声呐图像分类方法及其模型搭建方法 | |
CN117523549B (zh) | 基于深宽知识蒸馏的三维点云物体识别方法 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
TR01 | Transfer of patent right | ||
TR01 | Transfer of patent right |
Effective date of registration: 20221111 Address after: 221000 Building A13, Safety Technology Industrial Park, Tongshan District, Xuzhou City, Jiangsu Province Patentee after: XUZHOU GUANGLIAN TECHNOLOGY Co.,Ltd. Address before: 221116 Research Institute of China University of Mining and Technology, Tongshan University Road, Xuzhou City, Jiangsu Province Patentee before: CHINA University OF MINING AND TECHNOLOGY |
|
PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A Point Cloud Classification Method Based on Adaptive Feature Fusion of Point Cloud Graph Neural Network Effective date of registration: 20231106 Granted publication date: 20220426 Pledgee: Xuzhou Huaichang Investment Co.,Ltd. Pledgor: XUZHOU GUANGLIAN TECHNOLOGY Co.,Ltd. Registration number: Y2023980063946 |