CN103886555A - Processing method based on mass three-dimensional laser scanning point cloud data - Google Patents
Processing method based on mass three-dimensional laser scanning point cloud data Download PDFInfo
- Publication number
- CN103886555A CN103886555A CN201410088878.5A CN201410088878A CN103886555A CN 103886555 A CN103886555 A CN 103886555A CN 201410088878 A CN201410088878 A CN 201410088878A CN 103886555 A CN103886555 A CN 103886555A
- Authority
- CN
- China
- Prior art keywords
- data
- cloud data
- point cloud
- point
- cloud
- 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
Landscapes
- Length Measuring Devices With Unspecified Measuring Means (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention discloses a processing method based on mass three-dimensional laser scanning point cloud data, and relates to the field of data processing and three-dimensional digitalization. The method comprises the steps that sub sites upload collected point cloud data to a 3D display platform and adjust uploaded point density according to needs, an observing inspection method, a line inspection method and a string elevation difference method are adopted to remove noise points with large errors and find out possible distortion points, the point cloud data are simplified under the premise that the structure precision of a subsequent curved surface is guaranteed, smoothing filtering processing is carried out on the point cloud data by using an average filtering method and a Gaussian filtering method, local coordinate systems corresponding to sub site measurement are unified to a same coordinate system, and overlapped portions of two times of adjacent measurement are eliminated, and multiple pieces of point cloud data obtained through the sub site measurement are spliced together to obtain complete data of the surface of an object to be measured. The processing method based on the mass three-dimensional laser scanning point cloud data improves the processing speed of mass point cloud data, and the accuracy and the simplification degree of the point cloud data are improved.
Description
Technical field:
The present invention relates to data processing, three-dimensional digital field, belong to the part in three-dimensional data Processing Algorithm, be further processed data to collecting mass data, to improve data processing speed and data accuracy, call and lay the foundation for convenience of late time data.
Background technology:
The three-dimensional laser scanning technique outdoor scene reproduction technology that is otherwise known as is the technological revolution of survey field after GPS technology.It has broken through traditional spot measurement method, has high-level efficiency, high-precision unique advantage.Three-dimensional laser scanning technique can provide the three dimensional point cloud on scanning object surface, it is by the method for high-rate laser scanning survey, the three-dimensional coordinate data on quick obtaining measurand surface, large area high resolving power ground, collection point position in space information that can be quick, a large amount of, therefore can be for obtaining the digital model of high-accuracy high-resolution.Be widely used in fields such as Surveying Engineering, cultural relic digitalization protection, civil engineering work, commercial measurement, disaster investigation, digital city terrain visualization, urban and rural plannings
For the structure of model, the Processing Algorithm after data acquisition plays crucial effect.Data acquisition is subject to the impact of environment and system, and scan data volume is quite large, and cloud data is excessively intensive, inevitably has some redundant informations and interference noise point, can seriously affect speed and the precision of the structure of model.So the Processing Algorithm of mass data can ensure to obtain reliable selected cloud data after gathering, to improve data processing speed and data accuracy, reduce the difficulty that late time data calls, uses.
Summary of the invention:
For solving the not enough problem of available data processing power, the object of the present invention is to provide large scene magnanimity three dimensional point cloud post-processing algorithm, for magnanimity laser scanning data feature, reduce big data quantity and load the requirement to hardware, cancelling noise data point, carries out simplifying and smoothing processing of cloud data, finally all substation data amalgamation alignment, improve data processing speed and data accuracy, the calling of more convenient late time data.
Brief description of the drawings:
Fig. 1 is the three dimensional point cloud post-processing algorithm process flow diagram of patent of the present invention
embodiment:
Branch website loads the cloud data collecting in 3D display platform, and adjusts the dot density loading as required; Adopt and see inspection technique, the high difference method of ray examination method chord, remove the noise spot that those errors are large and find out the distorted spots that may exist; Ensureing that follow-up curved surface builds under the prerequisite of precision, simplifies cloud data; With mean filter and gaussian filtering method, cloud data is carried out to the disposal of gentle filter; Each substation is measured to corresponding local coordinate system unified to the same coordinate system, and eliminate adjacent 2 times and measure the lap of asking, substation is measured to the multi-disc point data splitting that obtains together, i.e. the amalgamation of cloud data alignment, to obtain the partial data on testee surface.
Searching of the rejecting of noise spot and distorted spots.In Non-contacted Three-dimensional Scanning Measurement process, be subject to the impact of the factors such as metering system, object being measured material character, external interference, inevitably can produce point (noise spot) and distorted spots (hop) that error is very large.Therefore in the first step of data processing, the denoising point function that just should utilize relevant special software to provide is removed the noise spot that those errors are large and is found out the distorted spots that may exist.
1. see inspection technique.By graphic display terminal, with the naked eye directly the acnode that departs from larger point with cross-section data point set or be present on screen is rejected.This method is suitable for the trial inspection of data, can from data point set, filter out some larger abnormity point.
2. ray examination method.By the first and last data point in cross section, obtain a SPL with least square fitting, order of a curve can determine according to the shape in curved surface cross section, be generally 3～4 rank, then calculate respectively the distance of intermediate data points pi to SPL || e||, if || e|| is more than or equal to [ε] ([ε] is given franchise), thinks that pi is bad point, should give rejecting.
3. action difference method.Connect front and back 2 points of checkpoint, calculate the distance of intermediate data points pi to string || e||, if || e|| is more than or equal to [ε] ([ε] is given franchise), thinks that pi is bad point, should give rejecting.This method is suitable for measurement point evenly and the occasion of comparatively dense, particularly changes position greatly in curvature.
Data compaction.The outstanding feature of Non-contacted Three-dimensional Scanning Measurement is that a cloud is very intensive, data volume extremely huge (having hundreds thousand of points in the scope of 1m2).If being directly used in to curved surface structure, huge data volume like this not only needs huge computer resource (common computer possibly cannot be competent at) and very long computing time, and whole processing procedure also will become and be difficult to control, be not that all test data is all useful to the structure of curved surface still more.Therefore, be necessary, under the prerequisite of the certain precision of guarantee, test data to be simplified.The principle of data compaction is to keep more data point in the scanning larger place of curvature, changes less place keep less data point in curvature.Dissimilar some cloud adopts the different modes of simplifying.Dispersion point cloud can be simplified by the method for stochastic sampling, and for sweep trace point cloud and polygon form point cloud can adopt equidistantly, the method such as multiplying power, equivalent and string deviation reduces the number of.
The smoothing processing of data.Stochastic error in cloud data, by having influence on the quality of structure and generating three-dimensional solid model of follow-up curved surface, therefore needed cloud data to carry out the disposal of gentle filter before building curved surface.The disposal of gentle filter has following 2 kinds of modes:
1. value filtering.The assembly average of getting each data point in filter window replaces original point, thereby changes the position of some cloud, makes a cloud level and smooth.Suppose that be respectively at adjacent 3, x0, x1 and x2, smoothly obtain new point by the mean filter, x ' 1, x ' 1=(x0+x1+x2)/3, straight line connected is newly put the point of representative after level and smooth.
2. gaussian filtering.Gaussian filtering be a kind of in specified domain the filtering method of filter away high frequency noise, be characterized in that the weight function in specified domain is Gaussian distribution.Because the average effect of gaussian filtering is less, therefore it can keep the original appearance of test data preferably.
Data amalgamation alignment.For completing the Non-contacted Three-dimensional Scanning Measurement to whole solid model, need to carry out substation measurement from each vision to solid model.Due in the time measuring zones of different, all to carry out under local coordinate system corresponding to measuring position, therefore repeatedly measure corresponding local coordinate system inconsistent, so it is unified to the same coordinate system to measure corresponding local coordinate system each time, and eliminate the lap between adjacent measurement for 2 times, to obtain the partial data on testee surface.This just need to measure substation the multi-disc point data splitting that obtains together, i.e. the amalgamation of cloud data alignment, and its disposal route has 2 kinds.The one, realize the amalgamation alignment of data by special measurement mechanism, it requires an automatic workpiece movable conversion platform of design, is used for directly recording amount of movement and the rotational angle of workpiece in measuring process; The 2nd, to realize the amalgamation alignment of multi-disc piece cloud data with custom-designed computer software, thereby realize the structure again of prototype, this is the most frequently used multi-disc piece point data splitting alignment schemes of present Non-contacted Three-dimensional Scanning Measurement.The cloud data that substation measures often can be regarded a rigid body as.Alignment of data can be summed up as the coordinate conversion problem of three-dimensional rigid body, according to some preassigned optimum matching rules, by coordinate transform, the ground alignment of partly overlapping two point cloud optimums.What in engineering, commonly use is the alignment schemes based on 3 reference points.Can determine a plane due to 3, therefore, in the time measuring, can in different views, set up 3 reference points for aliging, by these 3 reference points that align, just can realize many viewpoints of 3 d measurement data and unify.
Claims (5)
1. a large scene magnanimity three dimensional point cloud post-processing algorithm, is characterized in that, comprises the following steps: that successively branch website loads the cloud data collecting in 3D display platform, and adjusts the dot density loading as required; Adopt and see inspection technique, the high difference method of ray examination method chord, remove the noise spot that those errors are large and find out the distorted spots that may exist; Ensureing that follow-up curved surface builds under the prerequisite of precision, simplifies cloud data; With mean filter and gaussian filtering method, cloud data is carried out to the disposal of gentle filter; Each substation is measured to corresponding local coordinate system unified to the same coordinate system, and eliminate adjacent 2 times measure between lap, substation is measured to the multi-disc point data splitting that obtains together, i.e. the amalgamation of cloud data alignment, to obtain the partial data on testee surface.
2. a kind of first-phase treating algorithm for color three dimension dot clowd data according to claim 1, it is characterized in that, described ray examination method is the first and last data point by cross section, obtain a SPL with least square fitting, order of a curve can determine according to the shape in curved surface cross section, be generally 3～4 rank, then calculate respectively the distance of intermediate data points pi to SPL || e||, if || e|| is more than or equal to [ε] ([ε] is given franchise), think that pi is bad point, should give rejecting.
3. a kind of first-phase treating algorithm for color three dimension dot clowd data according to claim 1, it is characterized in that, described action difference method is front and back 2 points that connect checkpoint, calculate the distance of intermediate data points pi to string || e||, if || e|| is more than or equal to [ε] ([ε] is given franchise), think that pi is bad point, should give rejecting.
4. a kind of first-phase treating algorithm for color three dimension dot clowd data according to claim 1, is characterized in that, described mean filter method is that the assembly average of getting each data point in filter window replaces original point, thereby changes the position of some cloud, makes a cloud level and smooth.Suppose that be respectively at adjacent 3, x0, x1 and x2, smoothly obtain new point by the mean filter, x ' 1, x ' 1=(x0+x1+x2)/3, the point after level and smooth.
5. a kind of first-phase treating algorithm for color three dimension dot clowd data according to claim 1, it is characterized in that, described gaussian filtering be a kind of in specified domain the filtering method of filter away high frequency noise, be characterized in that the weight function in specified domain is Gaussian distribution, because the average effect of gaussian filtering is less, therefore it can keep the original appearance of test data preferably.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410088878.5A CN103886555A (en) | 2014-03-12 | 2014-03-12 | Processing method based on mass three-dimensional laser scanning point cloud data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410088878.5A CN103886555A (en) | 2014-03-12 | 2014-03-12 | Processing method based on mass three-dimensional laser scanning point cloud data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103886555A true CN103886555A (en) | 2014-06-25 |
Family
ID=50955430
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410088878.5A Pending CN103886555A (en) | 2014-03-12 | 2014-03-12 | Processing method based on mass three-dimensional laser scanning point cloud data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103886555A (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104616349A (en) * | 2015-01-30 | 2015-05-13 | 天津大学 | Local curved surface change factor based scattered point cloud data compaction processing method |
| CN104794747A (en) * | 2014-07-24 | 2015-07-22 | 西北农林科技大学 | Three-dimensional point cloud data simplification algorithm based on ray theory |
| CN105157656A (en) * | 2015-05-15 | 2015-12-16 | 天津智通机器人有限公司 | Blisk measurement path generation method |
| CN106372283A (en) * | 2016-08-24 | 2017-02-01 | 大连理工大学 | Method for processing actually measured three-dimensional morphology point cloud data of thin-wall shell obtained for digital photography |
| CN108459319A (en) * | 2018-03-13 | 2018-08-28 | 燕山大学 | A kind of quick scanning system of vehicle running region Terrain Elevation |
| CN108765571A (en) * | 2018-05-29 | 2018-11-06 | 大连九州创智科技有限公司 | A kind of large size material heap point cloud complementing method |
| CN108986048A (en) * | 2018-07-18 | 2018-12-11 | 大连理工大学 | Based on the quick compound filter processing method of line laser structured light three-dimensional point cloud |
| CN109102569A (en) * | 2018-06-13 | 2018-12-28 | 东莞时谛智能科技有限公司 | A kind of reconstruct foot point cloud model processing method and system |
| CN109166001A (en) * | 2018-07-24 | 2019-01-08 | 东华大学 | It is a kind of to pad design method towards personalized customized 3D moulding stern |
| CN111912346A (en) * | 2020-06-30 | 2020-11-10 | 成都飞机工业（集团）有限责任公司 | Nest hole online detection method suitable for robot drilling and riveting system on surface of airplane |
| CN111968224A (en) * | 2020-08-14 | 2020-11-20 | 南通诺德瑞海洋工程研究院有限公司 | Ship 3D scanning point cloud data processing method |
| CN114279364A (en) * | 2021-12-29 | 2022-04-05 | 江苏绿科船舶科技有限公司 | Novel scanning data registration method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102411778A (en) * | 2011-07-28 | 2012-04-11 | 武汉大学 | Automatic registration method of airborne laser point cloud and aerial image |
| CN103033805A (en) * | 2012-12-25 | 2013-04-10 | 西安煤航信息产业有限公司 | Automatic removal method for redundant data between air strips of airborne laser radar |
| CN103148804A (en) * | 2013-03-04 | 2013-06-12 | 清华大学 | Indoor unknown structure identification method based on laser scanning |
| CN103279980A (en) * | 2013-05-08 | 2013-09-04 | 西安理工大学 | Tree leaf modeling method based on point cloud data |
-
2014
- 2014-03-12 CN CN201410088878.5A patent/CN103886555A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102411778A (en) * | 2011-07-28 | 2012-04-11 | 武汉大学 | Automatic registration method of airborne laser point cloud and aerial image |
| CN103033805A (en) * | 2012-12-25 | 2013-04-10 | 西安煤航信息产业有限公司 | Automatic removal method for redundant data between air strips of airborne laser radar |
| CN103148804A (en) * | 2013-03-04 | 2013-06-12 | 清华大学 | Indoor unknown structure identification method based on laser scanning |
| CN103279980A (en) * | 2013-05-08 | 2013-09-04 | 西安理工大学 | Tree leaf modeling method based on point cloud data |
Non-Patent Citations (2)
| Title |
|---|
| 周儒荣: "逆向工程与快速原型制造", 《计算机辅助设计与制造》 * |
| 李刚: "基于逆向工程的自由曲面重构技术研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794747A (en) * | 2014-07-24 | 2015-07-22 | 西北农林科技大学 | Three-dimensional point cloud data simplification algorithm based on ray theory |
| CN104794747B (en) * | 2014-07-24 | 2017-11-21 | 西北农林科技大学 | A kind of three dimensional point cloud compressing method based on ray principle |
| CN104616349B (en) * | 2015-01-30 | 2017-07-28 | 天津大学 | Scattered point cloud data based on local surface changed factor simplifies processing method |
| CN104616349A (en) * | 2015-01-30 | 2015-05-13 | 天津大学 | Local curved surface change factor based scattered point cloud data compaction processing method |
| CN105157656A (en) * | 2015-05-15 | 2015-12-16 | 天津智通机器人有限公司 | Blisk measurement path generation method |
| CN105157656B (en) * | 2015-05-15 | 2017-11-07 | 天津智通机器人有限公司 | A kind of measuring route generation method of blisk |
| JP2019510975A (en) * | 2016-08-24 | 2019-04-18 | 大連理工大学 | Measured 3D profile point cloud data processing method of thin walled shell obtained by digital photography |
| CN106372283A (en) * | 2016-08-24 | 2017-02-01 | 大连理工大学 | Method for processing actually measured three-dimensional morphology point cloud data of thin-wall shell obtained for digital photography |
| WO2018036138A1 (en) * | 2016-08-24 | 2018-03-01 | 大连理工大学 | Method for processing actually measured three-dimensional morphology point cloud data of thin-wall shell obtained for digital photography |
| CN108459319A (en) * | 2018-03-13 | 2018-08-28 | 燕山大学 | A kind of quick scanning system of vehicle running region Terrain Elevation |
| CN108765571A (en) * | 2018-05-29 | 2018-11-06 | 大连九州创智科技有限公司 | A kind of large size material heap point cloud complementing method |
| CN109102569A (en) * | 2018-06-13 | 2018-12-28 | 东莞时谛智能科技有限公司 | A kind of reconstruct foot point cloud model processing method and system |
| CN108986048A (en) * | 2018-07-18 | 2018-12-11 | 大连理工大学 | Based on the quick compound filter processing method of line laser structured light three-dimensional point cloud |
| CN108986048B (en) * | 2018-07-18 | 2020-04-28 | 大连理工大学 | Three-dimensional point cloud rapid composite filtering processing method based on line laser scanning |
| CN109166001A (en) * | 2018-07-24 | 2019-01-08 | 东华大学 | It is a kind of to pad design method towards personalized customized 3D moulding stern |
| CN109166001B (en) * | 2018-07-24 | 2021-11-19 | 东华大学 | Personalized customized 3D shaping hip pad design method |
| CN111912346A (en) * | 2020-06-30 | 2020-11-10 | 成都飞机工业（集团）有限责任公司 | Nest hole online detection method suitable for robot drilling and riveting system on surface of airplane |
| CN111912346B (en) * | 2020-06-30 | 2021-12-10 | 成都飞机工业（集团）有限责任公司 | Nest hole online detection method suitable for robot drilling and riveting system on surface of airplane |
| CN111968224A (en) * | 2020-08-14 | 2020-11-20 | 南通诺德瑞海洋工程研究院有限公司 | Ship 3D scanning point cloud data processing method |
| CN114279364A (en) * | 2021-12-29 | 2022-04-05 | 江苏绿科船舶科技有限公司 | Novel scanning data registration method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103886555A (en) | Processing method based on mass three-dimensional laser scanning point cloud data | |
| CN105547151B (en) | Point Cloud Data from Three Dimension Laser Scanning acquires the method and system with processing | |
| Fassi et al. | Comparison between laser scanning and automated 3d modelling techniques to reconstruct complex and extensive cultural heritage areas | |
| CN109101709A (en) | The site construction management system that 3D laser scanner technique is combined with BIM technology | |
| CN106524920A (en) | Application of field measurement in construction project based on three-dimensional laser scanning | |
| Lovas et al. | Terrestrial laser scanning in deformation measurements of structures | |
| CN106767710A (en) | A kind of Earth Volume of Road Engineering measuring method and system | |
| CN101950433A (en) | Building method of transformer substation three-dimensional model by using laser three-dimensional scanning technique | |
| CN101793543A (en) | Research method for dynamically monitoring slope scale erosion development process by utilizing three-dimensional laser scanning technique | |
| CN105550428A (en) | Bridge security evaluation method based on TLS (three-dimensional laser scanning) technique | |
| CN102609940A (en) | Method for processing errors generated by point cloud registration in process of surface reconstruction of measuring object by using ground laser scanning technique | |
| CN104331927A (en) | Method and system for simulating digital outcrop surface of reservoir | |
| CN109990703A (en) | A kind of size detecting method and system of prefabricated components | |
| Macay Moreia et al. | From DSM to 3D building models: a quantitative evaluation | |
| CN109682303A (en) | BIM technology-based curve PC track beam precise three-dimensional detection method | |
| CN114693281B (en) | Engineering investigation information management system based on cloud platform | |
| CN111256730A (en) | Earth mass balance correction calculation method for low-altitude oblique photogrammetry technology | |
| CN106446415A (en) | Transformer-substation supporting insulator center line extracting method based on point cloud data | |
| Corradetti et al. | Quantitative analysis of folds by means of orthorectified photogrammetric 3D models: a case study from Mt. Catria, Northern Apennines, Italy | |
| CN113538668A (en) | Geological model construction method, geological model construction system, storage medium and electronic equipment | |
| CN115618472A (en) | Engineering structure digital pre-assembly method and system based on BIM model and application | |
| CN104062653A (en) | Radar remote sensing multi-scale surface roughness measuring method based on three-dimensional microtopography reconstruction | |
| CN114898053A (en) | Three-dimensional space image technology-based fractured loose rock mass development range delineation method | |
| CN117496103A (en) | Technical method for producing multi-mountain terrain area DEM by fusing unmanned aerial vehicle oblique photographing point cloud and terrain map elevation information | |
| Berényi et al. | Potential of terrestrial laserscanning in load test measurements of bridges |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| DD01 | Delivery of document by public notice |
Addressee: BEIJING HAOFENGDONGFANG TECHNOLOGY CO., LTD. Document name: the First Notification of an Office Action |
|
| DD01 | Delivery of document by public notice | ||
| DD01 | Delivery of document by public notice |
Addressee: BEIJING HAOFENGDONGFANG TECHNOLOGY CO., LTD. Document name: Notification that Application Deemed to be Withdrawn |
|
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20140625 |