WO2022017134A1 - 一种点云数据的处理方法、装置、电子设备及存储介质 - Google Patents
一种点云数据的处理方法、装置、电子设备及存储介质 Download PDFInfo
- Publication number
- WO2022017134A1 WO2022017134A1 PCT/CN2021/103037 CN2021103037W WO2022017134A1 WO 2022017134 A1 WO2022017134 A1 WO 2022017134A1 CN 2021103037 W CN2021103037 W CN 2021103037W WO 2022017134 A1 WO2022017134 A1 WO 2022017134A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- matrix
- grid
- convolution
- target
- processing
- Prior art date
Links
- 238000012545 processing Methods 0.000 title claims abstract description 199
- 238000000034 method Methods 0.000 title claims abstract description 77
- 239000011159 matrix material Substances 0.000 claims abstract description 405
- 238000003672 processing method Methods 0.000 claims abstract description 21
- 230000008569 process Effects 0.000 claims description 36
- 230000003628 erosive effect Effects 0.000 claims description 19
- 238000013527 convolutional neural network Methods 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 13
- 238000005530 etching Methods 0.000 claims 1
- AZFKQCNGMSSWDS-UHFFFAOYSA-N MCPA-thioethyl Chemical compound CCSC(=O)COC1=CC=C(Cl)C=C1C AZFKQCNGMSSWDS-UHFFFAOYSA-N 0.000 description 21
- 238000001514 detection method Methods 0.000 description 17
- 230000007797 corrosion Effects 0.000 description 13
- 238000005260 corrosion Methods 0.000 description 13
- 238000010586 diagram Methods 0.000 description 12
- 230000010339 dilation Effects 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
- G06T15/005—General purpose rendering architectures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
-
- 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
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
Definitions
- the generating a sparse matrix corresponding to the to-be-identified object according to the grid matrix and the size information of the to-be-identified object in the target scene includes: according to the grid matrix and For the size information of the object to be identified in the target scene, at least one expansion processing operation or erosion processing operation is performed on the target element in the grid matrix to generate a sparse matrix corresponding to the object to be identified; wherein, the The value of the target element indicates that the target point exists at the corresponding grid.
- performing a first inversion operation on the elements in the grid matrix before the current expansion processing operation to obtain the grid matrix after the first inversion operation includes: based on the second preset volume product kernel, perform convolution operation on other elements except the target element in the grid matrix before the current expansion processing operation, to obtain the first inversion element; based on the second preset convolution kernel, perform the current expansion processing operation on other elements
- the target element in the grid matrix before the operation is subjected to a convolution operation to obtain a second inversion element; based on the first inversion element and the second inversion element, the grid matrix after the first inversion operation is obtained.
- a generating module for generating a sparse matrix corresponding to the object to be identified according to the grid matrix and the size information of the object to be identified in the target scene; a determining module for generating a sparse matrix based on the generated matrix to determine the position of the object to be identified in the target scene.
- an embodiment of the present disclosure further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, any one of the first aspect and its various embodiments is executed.
- the steps of the processing method of point cloud data are stored, and when the computer program is executed by a processor, any one of the first aspect and its various embodiments is executed.
- FIG. 5 shows a schematic diagram of an apparatus for processing point cloud data according to Embodiment 2 of the present disclosure
- A'(0,0) and B'(0,0) are in the grid of the first row and the first column, and C'(2,3) can be in the grid of the second row and the third column.
- Gerry thus realizing the conversion from the Cartesian continuous real coordinate system to the discrete coordinate system.
- the coordinate information about the target point may be determined by a reference reference point (for example, the location of the radar device that collects the point cloud data), which will not be repeated here.
- the above-mentioned erosion processing operation may be implemented based on a shift operation and a logical AND operation, or may be implemented directly based on a convolution operation.
- the two operations use different methods, the final result of the generated sparse matrix can be the same.
- the expansion operation of the above-mentioned eight neighborhoods may be a process of determining an element whose absolute value of the difference between the abscissa or ordinate of the above-mentioned target element does not exceed 1. Except for the elements at the edge of the grid, generally all elements in the neighborhood of an element are There are eight elements (corresponding to the above element set), the input of the expansion processing result can be the coordinate information of the six target elements, and the output can be the coordinate information of the element set in the eight neighborhoods of the target element, as shown in FIG. 2B .
- the target element representing the existence of the target point at the corresponding grid can be shifted in multiple preset directions to obtain a plurality of corresponding shifted grid matrices.
- the grid matrix and the plurality of shifted grid matrices corresponding to the first expansion processing operation are logically ORed, so that the sparse matrix after the first expansion processing operation can be obtained.
- it can be judged whether the coordinate range of the obtained sparse matrix is less than The size of the object to be identified, and whether the corresponding difference is large enough (for example, greater than a preset threshold), if so, the target element in the sparse matrix after the first expansion processing operation can be shifted in multiple preset directions according to the above method.
- the corresponding preset directions of the shift processing are not the same.
- the grid matrix can be shifted according to the four preset directions.
- Bit processing which are left shift, right shift, up shift and down shift.
- the grid matrix can be shifted according to eight preset directions, respectively left shift, right shift. Move, move up, move down, move up and down under the premise of moving left, and move up and down under the premise of moving right.
- first perform a logical OR operation after determining the shifted grid matrix based on multiple shift directions, first perform a logical OR operation, and then perform multiple logical OR operations on the result. The shift operation in the shift direction is performed, and then the next logical OR operation is performed, and so on, until the dilated sparse matrix is obtained.
- embodiments of the present disclosure may combine the results of all neighborhoods using a matrix logical OR operation.
- Matrix logical OR operation that is, in the case of receiving two sets of zero-one matrix inputs with the same size, perform logical OR operation on the zero-one in the same position of the two sets of matrices in turn, and the obtained result forms a new zero-one matrix as the output,
- FIG. 3B A specific example of a logical OR operation is shown in FIG. 3B .
- the expansion processing operation can be implemented by combining convolution and two inversion processing. Specifically, the following steps can be implemented:
- the grid matrix after the first inversion operation is subjected to a convolution operation with the first preset convolution kernel to obtain the grid matrix after the first convolution operation.
- the grid matrix after the first convolution operation and the first preset convolution kernel can be convolved again to obtain the grid matrix after the second convolution operation. Lattice matrix, and so on, until a lattice matrix with a preset sparsity can be determined.
- At least one expansion processing operation or erosion processing operation is performed on the target element in the grid matrix to generate a sparse matrix corresponding to the object to be identified;
- the computer program product of the method for processing point cloud data provided by the embodiments of the present disclosure includes a computer-readable storage medium storing program codes, and the instructions included in the program codes can be used to execute the point clouds described in the above method embodiments.
- the steps of the data processing method reference may be made to the foregoing method embodiments, and details are not described herein again.
- the functions, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-executable non-volatile computer-readable storage medium.
- the computer software products are stored in a storage medium, including Several instructions are used to cause an electronic device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present disclosure.
- the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program codes .
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computer Graphics (AREA)
- General Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Image Analysis (AREA)
Abstract
Description
Claims (13)
- 一种点云数据的处理方法,所述方法包括:获取目标场景对应的点云数据;对获取的所述点云数据进行栅格化处理,得到栅格矩阵;所述栅格矩阵中每个元素的值用于表征对应的栅格处是否存在目标点,所述目标点表示所述点云数据对应的任一点;根据所述栅格矩阵以及所述目标场景中的待识别对象的尺寸信息,生成与所述待识别对象对应的稀疏矩阵;基于生成的所述稀疏矩阵,确定所述待识别对象在所述目标场景中的位置。
- 根据权利要求1所述的处理方法,其特征在于,所述根据所述栅格矩阵以及所述目标场景中的待识别对象的尺寸信息,生成与所述待识别对象对应的稀疏矩阵,包括:根据所述栅格矩阵以及所述目标场景中的待识别对象的尺寸信息,对所述栅格矩阵中的目标元素进行至少一次膨胀处理操作或者腐蚀处理操作,生成与所述待识别对象对应的稀疏矩阵;其中,所述目标元素的值表征对应的栅格处存在所述目标点。
- 根据权利要求2所述的处理方法,其特征在于,所述膨胀处理操作或者腐蚀处理操作包括移位处理以及逻辑运算处理,所述稀疏矩阵的坐标范围与所述待识别对象的尺寸之间的差值属于预设阈值范围。
- 根据权利要求2所述的处理方法,其特征在于,根据所述栅格矩阵以及所述目标场景中的待识别对象的尺寸信息,对所述栅格矩阵中的元素进行至少一次膨胀处理操作,生成与所述待识别对象对应的稀疏矩阵,包括:对当前次膨胀处理操作前的栅格矩阵中的元素进行第一取反操作,得到第一取反操作后的栅格矩阵;基于第一预设卷积核对所述第一取反操作后的栅格矩阵进行至少一次卷积运算,得到至少一次卷积运算后的具有预设稀疏度的栅格矩阵;对所述至少一次卷积运算后的具有预设稀疏度的栅格矩阵中的元素进行第二取反操作,得到所述稀疏矩阵。
- 根据权利要求4所述的处理方法,其特征在于,所述对当前次膨胀处理操作前的栅格矩阵中的元素进行第一取反操作,得到第一取反操作后的栅格矩阵,包括:基于第二预设卷积核,对当前次膨胀处理操作前的栅格矩阵中除所述目标元素外的其它元素进行卷积运算,得到第一取反元素;基于所述第二预设卷积核,对当前次膨胀处理操作前的栅格矩阵中的所述目标元素进行卷积运算,得到第二取反元素;基于所述第一取反元素和所述第二取反元素,得到第一取反操作后的栅格矩阵。
- 根据权利要求4或5所述的处理方法,其特征在于,所述基于第一预设卷积核对所述第一取反操作后的栅格矩阵进行至少一次卷积运算,得到至少一次卷积运算后的具有预设稀疏度的栅格矩阵,包括:针对首次卷积运算,将所述第一取反操作后的栅格矩阵与所述第一预设卷积核进行 卷积运算,得到首次卷积运算后的栅格矩阵;重复执行将上一次卷积运算后的栅格矩阵与所述第一预设卷积核进行卷积运算,得到当前次卷积运算后的栅格矩阵的步骤,直至得到具有所述预设稀疏度的栅格矩阵。
- 根据权利要求6所述的处理方法,其特征在于,所述第一预设卷积核具有权值矩阵以及与该权值矩阵对应的偏置量;所述针对首次卷积运算,将所述第一取反操作后的栅格矩阵与所述第一预设卷积核进行卷积运算,得到首次卷积运算后的栅格矩阵,包括:针对首次卷积运算,按照第一预设卷积核的尺寸以及预设步长,从所述第一取反操作后的栅格矩阵中选取每个栅格子矩阵;针对选取的每个所述栅格子矩阵,将该栅格子矩阵与所述权值矩阵进行乘积运算,得到第一运算结果,并将所述第一运算结果与所述偏置量进行加法运算,得到第二运算结果;基于各个所述栅格子矩阵对应的第二运算结果,确定首次卷积运算后的栅格矩阵。
- 根据权利要求2所述的处理方法,其特征在于,根据所述栅格矩阵以及所述目标场景中的待识别对象的尺寸信息,对所述栅格矩阵中的元素进行至少一次腐蚀处理操作,生成与所述待识别对象对应的稀疏矩阵,包括:基于第三预设卷积核对所述栅格矩阵进行至少一次卷积运算,得到至少一次卷积运算后的具有预设稀疏度的栅格矩阵;将所述至少一次卷积运算后的具有预设稀疏度的栅格矩阵,确定为与所述待识别对象对应的稀疏矩阵。
- 根据权利要求1至8任一所述的处理方法,其特征在于,对获取的所述点云数据进行栅格化处理,得到栅格矩阵,包括:对获取的所述点云数据进行栅格化处理,得到栅格矩阵以及该栅格矩阵中各个元素与各个目标点坐标范围信息之间的对应关系;所述基于生成的所述稀疏矩阵,确定所述待识别对象在所述目标场景中的位置,包括:基于所述栅格矩阵中各个元素与各个目标点坐标范围信息之间的对应关系,确定生成的所述稀疏矩阵中每个目标元素所对应的目标点的坐标信息;将所述稀疏矩阵中各个所述目标元素所对应的目标点的坐标信息进行组合,确定所述待识别对象在所述目标场景中的位置。
- 根据权利要求1至8任一所述的处理方法,其特征在于,所述基于生成的所述稀疏矩阵,确定所述待识别对象在所述目标场景中的位置,包括:基于已训练的卷积神经网络对生成的所述稀疏矩阵中的每个目标元素进行至少一次卷积处理,得到卷积结果;基于所述卷积结果,确定所述待识别对象在所述目标场景中的位置。
- 一种点云数据的处理装置,包括:获取模块,用于获取目标场景对应的点云数据;处理模块,用于对获取的所述点云数据进行栅格化处理,得到栅格矩阵;所述栅格 矩阵中每个元素的值用于表征对应的栅格处是否存在目标点,所述目标点表示所述点云数据对应的任一点;生成模块,用于根据所述栅格矩阵以及所述目标场景中的待识别对象的尺寸信息,生成与所述待识别对象对应的稀疏矩阵;确定模块,用于基于生成的所述稀疏矩阵,确定所述待识别对象在所述目标场景中的位置。
- 一种电子设备,包括处理器、存储器和总线,所述存储器存储有所述处理器可执行的机器可读指令,当电子设备运行时,所述处理器与所述存储器之间通过总线通信,所述机器可读指令被所述处理器执行时执行如权利要求1至10任一所述的点云数据的处理方法的步骤。
- 一种计算机可读存储介质,其上存储有计算机程序,该计算机程序被处理器运行时执行如权利要求1至10任一所述的点云数据的处理方法的步骤。
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
KR1020227007211A KR20220043186A (ko) | 2020-07-22 | 2021-06-29 | 포인트 클라우드 데이터의 처리 방법, 장치, 전자 기기 및 저장 매체 |
JP2022514519A JP2022546828A (ja) | 2020-07-22 | 2021-06-29 | 点群データ処理方法、装置、電子機器及び記憶媒体 |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010712674.X | 2020-07-22 | ||
CN202010712674.XA CN113971712A (zh) | 2020-07-22 | 2020-07-22 | 一种点云数据的处理方法、装置、电子设备及存储介质 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2022017134A1 true WO2022017134A1 (zh) | 2022-01-27 |
Family
ID=79584956
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2021/103037 WO2022017134A1 (zh) | 2020-07-22 | 2021-06-29 | 一种点云数据的处理方法、装置、电子设备及存储介质 |
Country Status (4)
Country | Link |
---|---|
JP (1) | JP2022546828A (zh) |
KR (1) | KR20220043186A (zh) |
CN (1) | CN113971712A (zh) |
WO (1) | WO2022017134A1 (zh) |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2014043764A1 (en) * | 2012-09-21 | 2014-03-27 | Umwelt (Australia) Pty. Limited | On-ground or near-ground discrete object detection method and system |
CN108399424A (zh) * | 2018-02-06 | 2018-08-14 | 深圳市建设综合勘察设计院有限公司 | 一种点云分类方法、智能终端及存储介质 |
CN109955486A (zh) * | 2019-03-14 | 2019-07-02 | 浙江大学 | 一种基于点阵化与稀疏压缩处理的结构模型3d打印方法 |
-
2020
- 2020-07-22 CN CN202010712674.XA patent/CN113971712A/zh active Pending
-
2021
- 2021-06-29 WO PCT/CN2021/103037 patent/WO2022017134A1/zh active Application Filing
- 2021-06-29 KR KR1020227007211A patent/KR20220043186A/ko unknown
- 2021-06-29 JP JP2022514519A patent/JP2022546828A/ja not_active Withdrawn
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2014043764A1 (en) * | 2012-09-21 | 2014-03-27 | Umwelt (Australia) Pty. Limited | On-ground or near-ground discrete object detection method and system |
CN108399424A (zh) * | 2018-02-06 | 2018-08-14 | 深圳市建设综合勘察设计院有限公司 | 一种点云分类方法、智能终端及存储介质 |
CN109955486A (zh) * | 2019-03-14 | 2019-07-02 | 浙江大学 | 一种基于点阵化与稀疏压缩处理的结构模型3d打印方法 |
Non-Patent Citations (2)
Title |
---|
LI FANGHUA: "A Research of Radar Target Localization Based on Compressed Sensing", MASTER THESIS, TIANJIN POLYTECHNIC UNIVERSITY, CN, no. 6, 15 June 2013 (2013-06-15), CN , XP055888363, ISSN: 1674-0246 * |
WANG HENG, WANG BIN, LIU BINGBING, MENG XIAOLI, YANG GUANGHONG: "Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle", ROBOTICS AND AUTONOMOUS SYSTEMS, ELSEVIER BV, AMSTERDAM, NL, vol. 88, 1 February 2017 (2017-02-01), AMSTERDAM, NL , pages 71 - 78, XP055794528, ISSN: 0921-8890, DOI: 10.1016/j.robot.2016.11.014 * |
Also Published As
Publication number | Publication date |
---|---|
JP2022546828A (ja) | 2022-11-09 |
CN113971712A (zh) | 2022-01-25 |
KR20220043186A (ko) | 2022-04-05 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109545072B (zh) | 地图构建的位姿计算方法、装置、存储介质和系统 | |
WO2022077863A1 (zh) | 视觉定位方法及相关模型的训练方法及相关装置、设备 | |
WO2022016942A1 (zh) | 一种目标检测方法、装置、电子设备及存储介质 | |
CN112990228B (zh) | 图像特征匹配方法和相关装置、设备及存储介质 | |
WO2022193335A1 (zh) | 点云数据处理方法、装置、计算机设备和存储介质 | |
CN113962858B (zh) | 一种多视角深度获取方法 | |
CN108364068B (zh) | 基于有向图的深度学习神经网络构建方法和机器人系统 | |
CN111985414B (zh) | 一种关节点位置确定方法及装置 | |
CN114004754A (zh) | 一种基于深度学习的场景深度补全系统及方法 | |
CN111553296B (zh) | 一种基于fpga实现的二值神经网络立体视觉匹配方法 | |
CN112825199B (zh) | 碰撞检测方法、装置、设备及存储介质 | |
JP2023541350A (ja) | 表畳み込みおよびアクセラレーション | |
CN116824092B (zh) | 三维模型生成方法、装置、计算机设备和存储介质 | |
WO2022017134A1 (zh) | 一种点云数据的处理方法、装置、电子设备及存储介质 | |
CN111402366B (zh) | 一种文字渲染方法、装置、电子设备及存储介质 | |
JP2022547873A (ja) | 点群データ処理方法及び装置 | |
CN113591969B (zh) | 面部相似度评测方法、装置、设备以及存储介质 | |
CN112561050B (zh) | 一种神经网络模型训练方法及装置 | |
CN114581676B (zh) | 特征图像的处理方法、装置和存储介质 | |
CN105117733A (zh) | 一种确定聚类样本差异的方法及装置 | |
CN109146886A (zh) | 一种基于深度密度的rgbd图像语义分割优化方法 | |
WO2022062451A1 (zh) | 一种处理三维数据的方法及设备 | |
CN113643200B (zh) | 基于递归图神经网络解决边缘过平滑的方法及装置 | |
CN111985542B (zh) | 代表性图结构模型、视觉理解模型的建立方法及应用 | |
CN113256757B (zh) | 基于反距离加权插值的等值线快速生成方法、设备及介质 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
ENP | Entry into the national phase |
Ref document number: 2022514519 Country of ref document: JP Kind code of ref document: A Ref document number: 20227007211 Country of ref document: KR Kind code of ref document: A |
|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 21845490 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 15.06.2023) |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 21845490 Country of ref document: EP Kind code of ref document: A1 |