CN112330803A - Urban building model generation method, system, equipment and medium - Google Patents
Urban building model generation method, system, equipment and medium Download PDFInfo
- Publication number
- CN112330803A CN112330803A CN202011237827.6A CN202011237827A CN112330803A CN 112330803 A CN112330803 A CN 112330803A CN 202011237827 A CN202011237827 A CN 202011237827A CN 112330803 A CN112330803 A CN 112330803A
- Authority
- CN
- China
- Prior art keywords
- building
- surface coating
- depth cloud
- contour
- sampling
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 35
- 239000011248 coating agent Substances 0.000 claims abstract description 52
- 238000000576 coating method Methods 0.000 claims abstract description 52
- 238000005070 sampling Methods 0.000 claims abstract description 41
- 230000000007 visual effect Effects 0.000 claims abstract description 14
- 239000007787 solid Substances 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 11
- 238000004364 calculation method Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000003384 imaging method Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T19/00—Manipulating 3D models or images for computer graphics
- G06T19/20—Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Graphics (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Geometry (AREA)
- Architecture (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Processing Or Creating Images (AREA)
Abstract
The invention discloses a method, a system, equipment and a medium for generating an urban building model, wherein the method comprises the following steps: acquiring a plurality of depth cloud images and a plurality of RGB images of a building along a plurality of sampling visual angles above the building, wherein each depth cloud image corresponds to one visual angle of the building; performing contour fitting according to the depth cloud pictures to obtain a three-dimensional model of the building; obtaining a surface coating of the stereoscopic model from the plurality of RGB images; attaching the surface coating to the surface of the three-dimensional model.
Description
Technical Field
The invention relates to a technology in the field of modeling, in particular to a method, a system, equipment and a medium for generating an urban building model.
Background
In urban planning management, urban buildings need to be modeled to form a real-scene model of a city, which is used for external display and engineering calculation management. At present, building modeling is mainly obtained through manual measurement or a large special imaging system, the manual measurement mode has the disadvantages of low measurement efficiency, low controllability of data precision and high uncertainty of observation errors, and the large special imaging system has the disadvantages of high price, complex system and poor portability.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method, a system, equipment and a medium for generating an urban building model, wherein an unmanned aerial vehicle can directly obtain a depth cloud picture of a building above the urban building through a ranging radar and obtain a corresponding RGB image through an image acquisition device to directly obtain a three-dimensional model with a real appearance pattern texture of the building, so that the three-dimensional model of the urban building can be rapidly obtained and can be visually displayed and used for engineering calculation.
According to an aspect of the present invention, there is provided a city building model generation method, including:
acquiring a plurality of depth cloud images and a plurality of RGB images of a building along a plurality of sampling visual angles above the building, wherein each depth cloud image corresponds to one visual angle of the building;
performing contour fitting according to the depth cloud pictures to obtain a three-dimensional model of the building;
obtaining a surface coating of the stereoscopic model from the plurality of RGB images;
attaching the surface coating to the surface of the three-dimensional model.
Preferably, the acquiring a plurality of depth cloud images and a plurality of RGB images of a building along a plurality of sampling viewing angles above the building comprises:
acquiring a first depth cloud image and a first RGB image of the building along a first sampling view angle;
acquiring a second depth cloud image and a second RGB image of the building along a second sampling view angle;
collecting a third depth cloud map and a third RGB image of the building along a third sampling perspective;
acquiring a fourth depth cloud map and a fourth RGB image of the building along a fourth sampling perspective.
Preferably, the included angles between the first sampling viewing angle, the second sampling viewing angle, the third sampling viewing angle and the fourth sampling viewing angle are all 90 degrees.
Preferably, the obtaining the stereoscopic model of the building by performing contour fitting according to the plurality of depth cloud images comprises:
performing contour fitting according to the first depth cloud image to obtain a first contour surface;
performing contour fitting according to the second depth cloud image to obtain a second contour surface;
performing contour fitting according to the third depth cloud image to obtain a third contour surface;
performing contour fitting according to the fourth depth cloud image to obtain a fourth contour surface;
splicing the first contour surface, the second contour surface, the third contour surface and the fourth contour surface to obtain the three-dimensional model of the building.
Preferably, the obtaining of the surface coating of the stereoscopic model from the plurality of RGB images comprises:
obtaining a first surface coating corresponding to the first contoured surface from the first RGB image;
obtaining a second surface coating corresponding to the first contour surface from the second RGB image;
obtaining a third surface coating corresponding to the first contour surface from the third RGB image;
obtaining a fourth surface coating corresponding to the first contoured surface from the fourth RGB image.
Preferably, the attaching the surface coating to the surface of the three-dimensional model comprises: attaching a first surface coating, the second surface coating, the third surface coating, and the fourth surface coating to the corresponding contoured surfaces, respectively.
According to one aspect of the present invention, there is provided a city building model generation method, comprising:
acquiring a plurality of depth cloud images and a plurality of RGB images of a building along a plurality of sampling visual angles by a unmanned aerial vehicle above the building, wherein each depth cloud image corresponds to one visual angle of the building;
the unmanned aerial vehicle carries out contour fitting according to the depth cloud pictures to obtain a three-dimensional model of the building;
the unmanned aerial vehicle obtains a surface coating of the three-dimensional model according to the RGB images;
the unmanned aerial vehicle attaches the surface coating to the surface of the three-dimensional model.
According to an aspect of the present invention, there is provided an urban building model generation system including:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring a plurality of depth cloud pictures and a plurality of RGB images of a building along a plurality of sampling visual angles above the building, and each depth cloud picture corresponds to one visual angle of the building;
the fitting module is used for carrying out contour fitting according to the depth cloud pictures to obtain a three-dimensional model of the building;
the coating module is used for obtaining a surface coating of the three-dimensional model according to the RGB images;
and the attaching module is used for attaching the surface coating to the surface of the three-dimensional model.
According to an aspect of the present invention, there is provided an urban building model generation apparatus including:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the above-described city building model generation method via execution of the executable instructions.
According to an aspect of the present invention, there is provided a computer-readable storage medium storing a program which, when executed, implements the steps of the above-described city building model generation method.
The beneficial effects of the above technical scheme are:
according to the urban building model generation method, the system, the equipment and the medium, the depth cloud picture of the building can be directly obtained through the ranging radar above the urban building by the unmanned aerial vehicle, and the three-dimensional model with the real exterior pattern texture of the building can be directly obtained through the corresponding RGB image obtained by the image acquisition device, so that the three-dimensional model of the urban building can be rapidly obtained, and can be visually displayed and used for engineering calculation.
Further features and advantages of the invention, as well as the structure and operation of various embodiments of the invention, are described in detail below with reference to the accompanying drawings. It should be noted that the present invention is not limited to the specific embodiments described herein. These examples are given herein for illustrative purposes only.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of an implementation scenario of the present invention;
FIG. 2 is a schematic flow chart of a method for generating a model of an urban building;
fig. 3 is a schematic diagram of the position of the drone at different sampling viewing angles;
FIG. 4 is a schematic view of a depth cloud image and RGB image acquisition process;
FIG. 5 is a schematic illustration of a process for stitching a three-dimensional model;
FIG. 6 is a schematic view of a surface coating acquisition process;
FIG. 7 is a block diagram of an urban building model generation system;
FIG. 8 is a schematic diagram of an urban building model generation facility of the present invention;
fig. 9 is a schematic structural diagram of a computer-readable storage medium of the present invention.
The features and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings. Throughout the drawings, like reference numerals designate corresponding elements. In the drawings, like reference numbers generally indicate identical, functionally similar, and/or structurally similar elements.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
As used in this application, the terms "first," "second," and the like do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
According to one aspect of the invention, a method for generating a model of an urban building is provided.
Fig. 1 is a schematic view of an implementation scenario of the present invention. Fig. 1 shows an implementation scenario 100 of the city building model generation method, and an unmanned aerial vehicle 102 is disposed above a building 101 shown in fig. 1, and the unmanned aerial vehicle 102 flies above the building 101. A drone 102 hovering over a building 101 is provided with a laser range radar 103 and a camera for the drone 102. The drone 102 uses the laser ranging radar 103 to capture depth cloud images of the building 101 and its camera to capture RGB images of the building 101.
FIG. 2 is a flow chart of a method for generating a model of an urban building. Fig. 3 is a schematic diagram of the positions of the drones at different sampling viewing angles. The city building model generation method shown in fig. 2 includes step S101, step S102, step S103, and step S104. In step S101, a plurality of depth cloud images and a plurality of RGB images of a building are acquired along a plurality of sampling viewing angles above the building, where each depth cloud image corresponds to a viewing angle of the building. In the step S102, carrying out contour fitting according to the depth cloud pictures to obtain a three-dimensional model of the building; in step S103, obtaining a surface coating of the three-dimensional model according to the plurality of RGB images; in step S104, a surface coating is attached to the surface of the solid model.
Fig. 4 is a schematic diagram of a depth cloud image and RGB image acquisition process. Referring to fig. 4, step S101 specifically includes: step S201, step S202, step S203, and step S204. In step S201, a first depth cloud image and a first RGB image of a building are collected along a first sampling viewing angle. In step S202, a second depth cloud image and a second RGB image of the building are collected along a second sampling perspective. In step S203, a third depth cloud image and a third RGB image of the building are collected along a third sampling viewing angle; in step S204, a fourth depth cloud image and a fourth RGB image of the building are collected along a fourth sampling perspective. Referring to fig. 3 and 4, the drone 102 acquires the corresponding depth cloud images and RGB images along the first sampling perspective 201, the second sampling perspective 202, the third sampling perspective 203, and the fourth sampling perspective 204 shown in fig. 3, respectively.
An RGB image is obtained by using an RGB color standard, and various colors are obtained by changing three color channels of red (R), green (G), and blue (B) and superimposing them on each other, and RGB is a color representing three channels of red, green, and blue. The depth cloud picture is formed by a plurality of points on the surface of a building obtained by a laser ranging radar in a space, namely the depth cloud picture is composed of the plurality of points obtained by the laser ranging radar, and each point has a depth value. The included angles between the first sampling view 201, the second sampling view 202, the third sampling view 203 and the fourth sampling view 204 are all 90 degrees, that is, the drone 102 collects the depth cloud image and the RGB image in four directions around the building 101.
Fig. 5 is a schematic diagram of a three-dimensional model stitching process. Referring to fig. 5, step S102 includes step S301, step S302, step S303, step S304, and step S305. In step S301, a first contour surface is obtained by performing contour fitting according to the first depth cloud image. And S302, performing contour fitting according to the second depth cloud image to obtain a second contour surface. In step S303, a third contour surface is obtained by performing contour fitting according to the third depth cloud image. In step S304, a fourth profile surface is obtained by performing profile fitting according to the fourth depth cloud image. In step S305, the first contour surface, the second contour surface, the third contour surface, and the fourth contour surface are spliced to obtain a three-dimensional model of the building.
FIG. 6 is a schematic view of a surface coating acquisition process. Referring to fig. 6, step S103 includes step S401, step S402, step S403, and step S404. In step S401, a first surface coating corresponding to the first contour surface is obtained from the first RGB image. In step S401, a first surface coating corresponding to the first contour surface is obtained from the first RGB image. In step S402, a second surface coating corresponding to the first contoured surface is obtained from the second RGB image. In step S403, a third surface coating corresponding to the first contour surface is obtained from the third RGB image. In step S404, a fourth surface coating corresponding to the first contour surface is obtained from the fourth RGB image. And finally, respectively attaching the first surface coating, the second surface coating, the third surface coating and the fourth surface coating to the corresponding contour surfaces, so as to form a final three-dimensional model.
FIG. 7 is a block diagram of an urban building model generation system. The system 300 shown in fig. 7 includes:
the acquisition module 301 acquires a plurality of depth cloud images and a plurality of RGB images of a building along a plurality of sampling view angles above the building, wherein each depth cloud image corresponds to one view angle of the building;
a fitting module 302, which performs contour fitting according to the depth cloud images to obtain a three-dimensional model of the building;
a coating module 303, which obtains a surface coating of the three-dimensional model according to the plurality of RGB images;
an attaching module 304 attaches the surface coating to the surface of the three-dimensional model.
According to an aspect of the present invention, there is provided an urban building model generation apparatus including: a processor; a memory having stored therein executable instructions of the processor; wherein the executable instructions, when executed, processor performs the steps of the city building model generation method.
FIG. 8 is a schematic diagram of the urban building model generation facility of the present invention. An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 8. The electronic device 600 shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 8, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting the different platform components (including the memory unit 620 and the processing unit 610), a display unit 640, etc.
Wherein the storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the above steps in this specification. For example, processing unit 610 may perform the steps as shown in fig. 2.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, to name a few.
According to an aspect of the present invention, there is provided a computer readable storage medium storing a program which, when executed, performs the steps of the above method.
Fig. 9 is a schematic structural diagram of a computer-readable storage medium of the present invention. Referring to fig. 9, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, according to the urban building model generation method, system, device and medium provided by the invention, the depth cloud picture of the building can be directly obtained by the ranging radar above the urban building through the unmanned aerial vehicle, and the three-dimensional model with the real exterior pattern texture of the building can be directly obtained by obtaining the corresponding RGB image through the image acquisition device, so that the three-dimensional model of the urban building can be rapidly obtained, and can be visually displayed and used for engineering calculation.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A method for generating a model of an urban building, comprising:
acquiring a plurality of depth cloud images and a plurality of RGB images of a building along a plurality of sampling visual angles above the building, wherein each depth cloud image corresponds to one visual angle of the building;
performing contour fitting according to the depth cloud pictures to obtain a three-dimensional model of the building;
obtaining a surface coating of the stereoscopic model from the plurality of RGB images;
attaching the surface coating to the surface of the three-dimensional model.
2. The urban building model generation method of claim 1, wherein the acquiring a plurality of depth clouds and a plurality of RGB images of a building along a plurality of sampling perspectives above the building comprises:
acquiring a first depth cloud image and a first RGB image of the building along a first sampling view angle;
acquiring a second depth cloud image and a second RGB image of the building along a second sampling view angle;
collecting a third depth cloud map and a third RGB image of the building along a third sampling perspective;
acquiring a fourth depth cloud map and a fourth RGB image of the building along a fourth sampling perspective.
3. The urban building model generation method according to claim 2, wherein the included angles between the first sampling perspective, the second sampling perspective, the third sampling perspective, and the fourth sampling perspective are all 90 degrees.
4. The urban building model generation method according to claim 2, wherein the obtaining a three-dimensional model of the building by contour fitting from the plurality of depth cloud maps comprises:
performing contour fitting according to the first depth cloud image to obtain a first contour surface;
performing contour fitting according to the second depth cloud image to obtain a second contour surface;
performing contour fitting according to the third depth cloud image to obtain a third contour surface;
performing contour fitting according to the fourth depth cloud image to obtain a fourth contour surface;
splicing the first contour surface, the second contour surface, the third contour surface and the fourth contour surface to obtain the three-dimensional model of the building.
5. The urban building model generation method according to claim 4, wherein the obtaining of the surface coating of the stereoscopic model from the plurality of RGB images comprises:
obtaining a first surface coating corresponding to the first contoured surface from the first RGB image;
obtaining a second surface coating corresponding to the first contour surface from the second RGB image;
obtaining a third surface coating corresponding to the first contour surface from the third RGB image;
obtaining a fourth surface coating corresponding to the first contoured surface from the fourth RGB image.
6. The urban building model generation method of claim 5, wherein the attaching the surface coating to the solid model surface comprises: attaching a first surface coating, the second surface coating, the third surface coating, and the fourth surface coating to the corresponding contoured surfaces, respectively.
7. A method for generating a model of an urban building, comprising:
acquiring a plurality of depth cloud images and a plurality of RGB images of a building along a plurality of sampling visual angles by a unmanned aerial vehicle above the building, wherein each depth cloud image corresponds to one visual angle of the building;
the unmanned aerial vehicle carries out contour fitting according to the depth cloud pictures to obtain a three-dimensional model of the building;
the unmanned aerial vehicle obtains a surface coating of the three-dimensional model according to the RGB images;
the unmanned aerial vehicle attaches the surface coating to the surface of the three-dimensional model.
8. An urban building model generation system, comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring a plurality of depth cloud pictures and a plurality of RGB images of a building along a plurality of sampling visual angles above the building, and each depth cloud picture corresponds to one visual angle of the building;
the fitting module is used for carrying out contour fitting according to the depth cloud pictures to obtain a three-dimensional model of the building;
the coating module is used for obtaining a surface coating of the three-dimensional model according to the RGB images;
and the attaching module is used for attaching the surface coating to the surface of the three-dimensional model.
9. An urban building model generation device, comprising:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the city building model generation method of any one of claims 1-8 via execution of the executable instructions.
10. A computer-readable storage medium storing a program, wherein the program when executed implements the steps of the city building model generation method of any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011237827.6A CN112330803A (en) | 2020-11-09 | 2020-11-09 | Urban building model generation method, system, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011237827.6A CN112330803A (en) | 2020-11-09 | 2020-11-09 | Urban building model generation method, system, equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112330803A true CN112330803A (en) | 2021-02-05 |
Family
ID=74317205
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011237827.6A Pending CN112330803A (en) | 2020-11-09 | 2020-11-09 | Urban building model generation method, system, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112330803A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1374620A (en) * | 2002-03-27 | 2002-10-16 | 深圳市特得维技术有限公司 | Computerized 3D visual color scan system and its scanning mode |
| CN101581575A (en) * | 2009-06-19 | 2009-11-18 | 南昌航空大学 | Three-dimensional rebuilding method based on laser and camera data fusion |
| CN101901501A (en) * | 2010-07-22 | 2010-12-01 | 首都师范大学 | Method for generating laser color cloud picture |
| US20170301104A1 (en) * | 2015-12-16 | 2017-10-19 | Objectvideo, Inc. | Profile matching of buildings and urban structures |
| CN107452061A (en) * | 2017-06-28 | 2017-12-08 | 百度在线网络技术（北京）有限公司 | Generation method, device, equipment and the computer-readable recording medium of building model based on oblique photograph technology |
| CN111415409A (en) * | 2020-04-15 | 2020-07-14 | 北京煜邦电力技术股份有限公司 | Modeling method, system, equipment and storage medium based on oblique photography |
-
2020
- 2020-11-09 CN CN202011237827.6A patent/CN112330803A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1374620A (en) * | 2002-03-27 | 2002-10-16 | 深圳市特得维技术有限公司 | Computerized 3D visual color scan system and its scanning mode |
| CN101581575A (en) * | 2009-06-19 | 2009-11-18 | 南昌航空大学 | Three-dimensional rebuilding method based on laser and camera data fusion |
| CN101901501A (en) * | 2010-07-22 | 2010-12-01 | 首都师范大学 | Method for generating laser color cloud picture |
| US20170301104A1 (en) * | 2015-12-16 | 2017-10-19 | Objectvideo, Inc. | Profile matching of buildings and urban structures |
| CN107452061A (en) * | 2017-06-28 | 2017-12-08 | 百度在线网络技术（北京）有限公司 | Generation method, device, equipment and the computer-readable recording medium of building model based on oblique photograph technology |
| CN111415409A (en) * | 2020-04-15 | 2020-07-14 | 北京煜邦电力技术股份有限公司 | Modeling method, system, equipment and storage medium based on oblique photography |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10482659B2 (en) | System and method for superimposing spatially correlated data over live real-world images | |
| Zollmann et al. | Flyar: Augmented reality supported micro aerial vehicle navigation | |
| RU2741443C1 (en) | Method and device for sampling points selection for surveying and mapping, control terminal and data storage medium | |
| CN111415409B (en) | Modeling method, system, equipment and storage medium based on oblique photography | |
| CN109387186A (en) | Mapping information acquisition methods, device, electronic equipment and storage medium | |
| KR20190013384A (en) | Ar and vr structure modeling system based on space data according to site condition | |
| CN107863049A (en) | A kind of finishing outpost display system based on line holographic projections technology | |
| CN115641401A (en) | Construction method and related device of three-dimensional live-action model | |
| CN114758337B (en) | Semantic instance reconstruction method, device, equipment and medium | |
| CN112383746A (en) | Video monitoring method and device in three-dimensional scene, electronic equipment and storage medium | |
| CN112053440A (en) | Method for determining individualized model and communication device | |
| Koeva | 3D modelling and interactive web-based visualization of cultural heritage objects | |
| CN115825067A (en) | Geological information acquisition method and system based on unmanned aerial vehicle and electronic equipment | |
| CN111527375B (en) | Planning method and device for surveying and mapping sampling point, control terminal and storage medium | |
| CN112652062A (en) | Point cloud map construction method, device, equipment and storage medium | |
| CN113077500A (en) | Panoramic viewpoint positioning and attitude determining method, system, equipment and medium based on plane graph | |
| US20200257832A1 (en) | System and method for generating floor plans | |
| CN115294283B (en) | Digital twin plant construction method, device, equipment and storage medium | |
| JP7375149B2 (en) | Positioning method, positioning device, visual map generation method and device | |
| CN112329649A (en) | Urban vegetation type identification method, system, equipment and medium | |
| CN112330803A (en) | Urban building model generation method, system, equipment and medium | |
| Pratama et al. | An Overview of Generating VR Models for Disaster Zone Reconstruction Using Drone Footage | |
| CN115546422A (en) | Building three-dimensional model construction method and system and electronic equipment | |
| US20240005556A1 (en) | System and method for assessing imaged object location | |
| CN113160309A (en) | Panoramic image positioning and attitude determining method, system, equipment and medium based on ground line |
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 |