CN103969656A - Building modeling method and device based on airborne laser radar - Google Patents
Building modeling method and device based on airborne laser radar Download PDFInfo
- Publication number
- CN103969656A CN103969656A CN201410193508.8A CN201410193508A CN103969656A CN 103969656 A CN103969656 A CN 103969656A CN 201410193508 A CN201410193508 A CN 201410193508A CN 103969656 A CN103969656 A CN 103969656A
- Authority
- CN
- China
- Prior art keywords
- point
- building
- buildings
- line
- convex hull
- 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
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Electromagnetism (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Image Processing (AREA)
Abstract
The invention provides a building modeling method and device. The method includes the steps that airborne LIDAR data are preprocessed, and building point cloud information is extracted in non-ground points; the point cloud information is partitioned to obtain a building object; building regular frames are extracted; roof points are classified according to surface patches; a building model is established according to the classification result; the effect of the established model is displayed. By means of the building modeling method and device, the precision of the building model is greatly improved, and the degree of automation of building extraction is further improved.
Description
Technical field
The present invention relates to satellite remote sensing and graphical modeling, particularly a kind of Building Modeling method and apparatus based on airborne laser radar.
Background technology
The rise of laser radar (Light Detection And Ranging, be called for short LIDAR) technology provides new selection for solving the how problem of quick obtaining data.Laser radar technique belongs to a kind of active technology for information acquisition, from occurring that oneself has passed through development for many years so far, and rises the attention that is more and more subject to relevant industries gradually.Lidar measurement technology is a new technique that occurs and progressively grow up from the middle and later periods in 20th century, airborne LIDAR is an integrated system, comprise multiple equipment, mainly contain laser ranging unit, optical-mechanical scanning element, control record cell, GPS receiver and Inertial Measurement Unit IMU (InertialMeasure Unit) etc., wherein laser ranging unit has comprised again generating laser and receiver.Laser ranging unit is mainly used for measuring the distance between generating laser and target reflection spot; Optical-mechanical scanning element can realize airborne LIDAR target surface is carried out to point-to-point measurement along certain direction; GPS receiver and IMU are mainly used to obtain attitude and the position of platform, these two equipment navigational system that is otherwise known as of joining together, or POS (Positioning andOrientation system) system, resolve acquisition point cloud data space information by the combination of these equipment.
Urban area Building Modeling, has very important meaning for the foundation of digital city.The classic method that three-dimensional digital city rebuilds is mainly to obtain the three-dimensional model of buildings by aviation image Stereo Matching Technology.But still have many problems not solve well, this also impels researcher to go exploration new data acquisition technology or new data processing method to carry out the reconstruction of buildings.The developing into us and obtain city space information a kind of brand-new technological means is provided of airborne LIDAR measuring technique, in the cloud data that wherein airborne LIDAR obtains, include a large amount of building space information, because this part building information of principle of work of airborne LIDAR is again mainly taking building roof information as main.Therefore the modeling of buildings in digital city that develops into of airborne laser radar provides strong support, and the management to the correlative study of airborne LIDAR to city and daily life are all significant.
A kind of conventional method that current airborne laser radar is counted buildings extraction is the digital image processing method based on traditional.In the evolution of remote sensing, accumulate the algorithm of many pattern-recognitions and digital image processing, wherein had certain methods still can be incorporated into the Data processing to airborne laser radar.Common way is in discrete laser radar point cloud, to be inserted as graticule mesh, then according to the method for image processing, it is processed and is analyzed, and finds buildings border, obtains building model in conjunction with landform.Because this method is that laser radar point cloud data is used as to image processing, thus in the process of interpolation, lose precision, and do not utilize well the feature of airborne laser radar point cloud data itself.In precision, there is larger error in the building model therefore obtaining.
Also having a kind of method is the method based on model-driven.Build building model based on LIDAR cloud data model-driven, need building type and the building pattern of known structure point cloud object, and then in model bank, select corresponding model building method, choose or calculate corresponding parameter and obtain building model.But type and building pattern that this must know buildings in advance, can only carry out modeling to existing buildings in model bank.Therefore adopting the mode of model-driven to build building model suffers restraints more serious at the aspect such as availability and robotization.
Therefore,, for existing the problems referred to above in correlation technique, effective solution is not yet proposed at present.
Summary of the invention
For solving the existing problem of above-mentioned prior art, the present invention proposes a kind of method of carrying out Building Modeling based on airborne laser radar data.
The present invention adopts following technical scheme: a kind of method of Building Modeling, comprises the following steps:
Step 1: airborne LIDAR data are carried out to pre-service, be included in cloud data ground point is separated with non-ground point;
Step 2: extract building object point cloud information in described non-ground point;
Step 3: a cloud information is cut apart, obtained buildings object;
Step 4: extract the buildings rule frame in buildings object;
Step 5: dough sheet is pressed in the roof of buildings object and classify;
Step 6: according to described classification results, set up building model;
Step 7: show the modelling effect of setting up.
Preferably, described step 3 utilizes rays method to cut apart a cloud information, obtains the buildings object of separation mutually,
Wherein, in the cutting procedure of rays method, adopt and draw polygon, utilize singularity and the parity of ray and polygon focus, whether judging point is in polygon inside.
Preferably, described step 4 further comprises, based on the process of extracting contour of building line, in extracting contour of building line point, has recorded the order of outline line point,
Described extraction contour of building line further comprises:
Outline line point using the concentrated convex hull summit obtaining as buildings, projects to point set in XOY plane,
Step 4.1: determine first convex hull point of buildings, selected point of the present invention is concentrated the starting point that the point of x coordinate minimum is convex hull, if there is the point that x coordinate is identical, the starting point that the point of getting y coordinate minimum is convex hull,
Step 4.2: in search when next outline line point, left as initial direction, record start direction is put the angle of centrostigma and the line segment being connected of starting point to son taking level, the convex hull point that the point of angle minimum is this sub-point set, is the encirclement shell point of buildings,
Step 4.3: if do not determine sub-point set, expand successively sweep limit, surround shell point until obtain, centered by the point just having obtained, determine sub-point set,
Step 4.4: the line of the encirclement shell point obtaining taking the current encirclement shell point obtaining and last time is as initial direction, determines that son puts concentrated convex hull point, as the point of contour of building line,
Step 4.5: repeating step 4.2-4.4, until obtain all frontier points.
Preferably, described encirclement shell is further by following Procedure Acquisition:
(a) input buildings discrete point cloud data collection S;
(b) set up the Grid Index of discrete point cloud data;
(c) determine starting point, i.e. lower-left point A;
(d) taking starting point as the center of circle, 1.5-2 times of dot spacing is radius, determines current scope Point Set S1={P
1, P
2... P
ns1={P
1, P
2..., P
n, wherein P
1-p
nfor).
(e) from point set S, determine and surround shell point P_ (B), wherein P_ (B) ∈ S1.
(f), with the current point of P_ (B), repeating step c, d and e, determine a lower P_ (B).
(g) until current some P_ (B) is identical with some A,, outline line point extracts complete, and process finishes.
Preferably, described step 5 further comprises:
Whether exceed setting threshold according to large class point proportion, will isolate wrong class point and be corrected as large class type, to optimize the classification of triangulation network normal vector.
Preferably, described step 6 further comprises:
According to the buildings dough sheet sorting out, each dough sheet is simulated to corresponding plane equation,
According to the buildings regular borders of having obtained, set up between each dough sheet and with the topological relation of the each line segment of regular borders,
Ask the intersection of face and face according to topological relation, the intersection point of line and line,
The unique point that utilization calculates and corresponding topological relation construct building model.
According to a further aspect in the invention, provide a kind of device for Building Modeling, having comprised:
Data preprocessing module, for airborne LIDAR data are carried out to pre-service, is included in cloud data ground point is separated with non-ground point;
Point cloud information extraction modules, for extracting building object point cloud information at non-ground point;
Cut apart module, for utilizing rays method to cut apart a cloud information, obtain the buildings object of mutually separating;
Frame extraction module, for extracting the regular frame of buildings;
Roof point sort module, classifies for dough sheet is pressed in roof;
Model building module, for setting up building model according to classification results;
Display module, for showing set up modelling effect at openGL.
Than prior art, having the following advantages of technical scheme of the present invention: the measuring method adopting with respect to traditional urban house modeling, speed is faster undoubtedly, efficiency is higher to use airborne laser radar skill modeling method provided by the invention, save a large amount of manpower and materials, will improve to a great extent the automaticity of 3 d modeling of building.With respect to the Building Modeling method of laser radar point cloud data being used as to image processing, the present invention, directly from cloud data, uses rays method to carry out man-machine interaction to buildings and cuts apart, and has greatly improved the precision of building model.And with respect to the method for model-driven, the present invention is directed to the shortcoming of its type that need to prejudge buildings and building pattern, the method of dough sheet classification is pressed on the extraction and the roof that have proposed buildings rule frame, effectively raises the automaticity that buildings extracts.
The present invention proposes a set of complete method of carrying out 3 d modeling of building based on airborne laser radar data, along with airborne LIDAR data increasingly extensive obtain, the present invention has very strong practical value, will provide technical support for the application of laser radar in the modeling of digital city.
Brief description of the drawings
Fig. 1 is the Building Modeling method flow diagram according to the embodiment of the present invention.
Fig. 2 and Fig. 3 utilize the schematic diagram of rays method to two kinds of situations of point and polygon relation according to the embodiment of the present invention.
Fig. 4 is according to the schematic diagram of the nail of the embodiment of the present invention and convex hull theory.
Fig. 5-8th, according to the schematic diagram of the improved convex hull algorithm process of the embodiment of the present invention.
Fig. 9 is according to the test result figure of the outline line extracting method of the embodiment of the present invention.
Embodiment
Various ways can be for (comprising the process of being embodied as; Device; System; Material composition; The computer program comprising on computer-readable recording medium; And/or processor (such as following processor, this processor is configured to carry out instruction that store on the storer that is coupled to processor and/or that provided by this storer)) enforcement the present invention.In this manual, any other form that these enforcements or the present invention can adopt can be called technology.Generally speaking, can change within the scope of the invention the sequence of steps of disclosed process.Unless separately had and expressed, the parts (such as processor or storer) that are described as being configured to execute the task may be embodied as by provisional configuration to become in preset time to carry out the general parts of this task or be manufactured into the concrete parts of carrying out this task.
Below provide the detailed description to one or more embodiment of the present invention together with illustrating the accompanying drawing of the principle of the invention.Describe the present invention in conjunction with such embodiment, but the invention is not restricted to any embodiment.Scope of the present invention is only defined by the claims, and the present invention contain manyly substitute, amendment and equivalent.Set forth in the following description many details to provide thorough understanding of the present invention.These details are provided for exemplary purposes, and also can realize the present invention according to claims without some or all details in these details.
The object of the invention is to overcome the existing defect of utilizing laser radar point cloud data to carry out buildings extraction and modeling, in conjunction with rays method cut apart, regular frame extract and roof point dough sheet sorting algorithm, propose a set of complete method of carrying out 3 d modeling of building based on airborne laser radar data, overcome problems of the prior art
The present invention is directly from cloud data, using rays method to carry out man-machine interaction to buildings cuts apart, greatly improve the precision of building model, thereby overcome, laser radar point cloud data has been used as in the process of method interpolation of image processing and is lost precision, the problem that error is larger.On the other hand, the method that the present invention proposes to use the extraction of buildings rule frame and roof to press dough sheet classification is carried out the extraction of building type and building pattern automatically, effectively raise the automaticity that buildings extracts, overcome the serious constraint of the aspect such as availability and robotization, thereby finally realized the foundation of building model.
Particularly, the method for Building Modeling of the present invention comprises the following steps:
(1) data pre-service.The error that this process comprises cloud data is rejected and filtering, and realizes ground point and non-topocentric separation.
(2) in non-ground point, extract building object point cloud information.
(3) utilize rays method to cut apart a cloud information, obtain the buildings object of mutually separating.
Due to the existence of error in auto Segmentation process, for example two houses distance is too near, noise spot between house etc.Therefore after cutting apart, individual building may be divided into same building with another or multiple other buildingss, causes the form between buildings and real building thing to have larger gap, so cutting procedure needs man-machine interaction to cut apart buildings.
Particularly, the present invention adopts drafting polygon, and judging point is manually cut apart in the method for polygon inside.Existing judging point is mainly rays method in the method for polygon inside, the theoretical thought of rays method is: to treat that judging point does a ray to arbitrary definite direction as end points, judge ray and draw polygonal intersection point number, in the time that number of hits is odd number judging point in polygon inside, in the time that intersection point number is even number judging point in polygonal outside (as shown in Figure 2), in Fig. 2, be easy to determine A point in polygon inside according to this theory, B point is at outside of polygon.But adopt this rays method to understand some special circumstances and cannot judge (as shown in Figure 3), in Fig. 3 as in the time that A point and polygon have three intersection points to be odd number, should be in polygon inside according to rays method, but actual conditions its outside polygon, B point in like manner draws the conclusion of contradiction.
Whether therefore the present invention is to having made some improvements on the discriminant approach of rays method, by utilizing the singularity of ray and polygon focus and parity judging point in polygon inside, the leak of above-mentioned rays method is made up perfect, improves the precision of manually cutting apart.
(4) the regular frame of extraction buildings.
The extraction of the contour of building line based on original point cloud data above only extracts the point on border from raw data, and the ordinal relation between the point on border is not expressed, for subsequent treatment is made troubles.The present invention has improved based on the theoretical method of extracting contour of building line of convex hull, in extracting contour of building line point, has recorded the order of outline line point.
Convex hull theory can be carried out vivid description like this, we nail on many nail representative points in the above a plank, then get a bungee and be enclosed within on nail, now the shape of bungee after being stretched is exactly the convex hull (as shown in Figure 4) of these nails.The Ao shell border of plane point set is a convex polygon, and its summit is the concentrated point of point, and convex hull border is the minimum polygon that surrounds point set, is the convex polygon with minimum perimeter polygon and sealing.Due to not convex polygon always of the outline line of buildings, therefore, be only the outline line that can not express buildings with convex polygon, the present invention has improved the method for extracting thought and proposing to surround shell summit from convex hull, the border that can effectively extract buildings.
The method of the outline line based on surrounding shell algorithm extraction buildings is in certain limit, to put the outline line point of the concentrated convex hull summit obtaining as buildings, the outline line point obtaining is like this not the convex hull of buildings point set, but can embody the outline line of buildings.Because building roof point centrostigma is three-dimensional, the present invention projects to point set the planimetric coordinates of only considering point set in XOY plane in the time of Extracting contour.The same with convex hull algorithm, first convex hull point of first definite buildings, selected point of the present invention is concentrated the starting point that the point of x coordinate minimum is convex hull, if there is the point that x coordinate is identical, gets the point of y coordinate minimum, i.e. lower-left point.Easily understand, lower-left point must be a summit of convex hull, as shown in red point in Fig. 5.In the time of the next outline line point of search, different from convex hull algorithm is, convex hull algorithm is that the concentrated point of whole point is detected object, and investigative range has been done certain circumscription by the present invention, as shown in Figure 6, taking level, left as initial direction, record start direction is the angle (clockwise) with the line segment being connected of starting point to son point centrostigma, the convex hull point (Fig. 6 medium green color dot) that the point of angle minimum is this sub-point set, is the encirclement shell point of buildings.If determine sub-point set, expand sweep limit successively until obtain encirclement shell point (as red circle in Fig. 6).Then, centered by the point just having obtained, determine sub-point set, taking the line of this point and the point that obtained last time as initial direction, determine the concentrated convex hull point of son point, as the point of contour of building line.As shown in Figure 7.According to above-mentioned steps, until obtain all frontier points, shown in Fig. 8.
Surround shell acquisition process
By foregoing description, adopt the summit that surrounds shell as contour of building line point, therefore, need to determine search subset scope.In the time determining sub-point set, need to concentrate and search point of proximity from buildings point, adopt planar grid index organization data, be convenient to search for point of proximity.Definite mode of sub-point set is that centered by the outline line point of determining, the point in certain radius region is as sub-point set.This radius is traditionally arranged to be the dot spacing of 1.5-2 times, the performance contour of building that the outline line point drawing like this can be compacter.The concrete acquisition process step of surrounding shell is as follows:
(a) input buildings discrete point cloud data collection S;
(b) set up the Grid Index of discrete point cloud data;
(c) determine starting point, i.e. lower-left point A point;
(d) taking starting point as the center of circle, 1.5-2 times of dot spacing is radius, determines scope Point Set S1={P
1, P
2... P
n(P
nfor point).
(e) adopt said method, from point set S, determine and surround shell point P_ (B), wherein P_ (B) ∈ S1.
(f), with the current point of P_ (B), repeating step c, d and e, determine a lower P_ (B).
(g) until current some P_ (B) is identical with A point, algorithm finishes, and outline line point extracts complete.
Fig. 9 is the test result figure that adopts above-mentioned algorithm.The left side is point set, the outline line point of the red point in the right for adopting the method to obtain, and as can be seen from Figure 9, this algorithm can effectively extract the encirclement shell point of buildings.
(5) dough sheet being pressed in roof classifies.
Existing triangulation network normal vector method is to a dough sheet classification, because the existence of error can cause putting individually classification error, mixing other classification points classifying substantially correct in the situation that, but in fact these error points that mix should be also to belong to main body classification point around.But classification error point large degree affect dough sheet segmentation effect and follow-up data processing precision.
Therefore for this situation, algorithm is improved, by whether exceeding setting threshold according to large class point proportion in certain area, a small amount of or isolated wrong class point is corrected as to large class type.Correct a small amount of error point in existence and large classification, optimized the classification results of existing triangulation network normal vector.
(6) set up building model.
According to the buildings dough sheet sorting out, each dough sheet is simulated to corresponding plane equation, according to the buildings regular borders of having obtained, set up between each dough sheet and with the topological relation of the each line segment of regular borders, ask the intersection of face and face according to topological relation, the intersection point of line and line, the final unique point and the corresponding topological relation that calculate of utilizing constructs building model.
(7) in openGL, show set up modelling effect.
In implementation process, the relevant improvement of pre-service and the modeling of later stage buildings etc. of the present invention by cloud data, in conjunction with domestic be main mainly with one-storey house, herringbone room, four Fang HeLXing rooms, slope, the building model of these several classes is rebuild.In building object point cloud, sort out a certain buildings, then call such Model Reconstruction algorithm, realize the modeling of buildings.
According to a further aspect in the invention, provide a kind of device for Building Modeling, having comprised:
Data preprocessing module;
Point cloud information extraction modules, for extracting building object point cloud information at non-ground point;
Cut apart module, for utilizing rays method to cut apart a cloud information, obtain the buildings object of mutually separating;
Frame extraction module, for extracting the regular frame of buildings;
Roof point sort module, classifies for dough sheet is pressed in roof;
Model building module, for setting up building model according to classification results;
Display module, for showing set up modelling effect at openGL.
In sum, the present invention proposes a set of complete method and apparatus that carries out 3 d modeling of building based on airborne laser radar data, along with airborne LIDAR data increasingly extensive obtain, the present invention has very strong practical value, will provide technical support for the application of laser radar in the modeling of digital city.With respect to traditional modeling method, speed is faster undoubtedly, efficiency is higher for airborne laser radar skill modeling method provided by the invention, save a large amount of manpower and materials, the automaticity of 3 d modeling of building will be improved to a great extent, directly from cloud data, use rays method to carry out man-machine interaction to buildings and cut apart, greatly improved the precision of building model.Need to prejudge the type of buildings and the shortcoming of building pattern for it, the method for dough sheet classification is pressed on the extraction and the roof that have proposed buildings rule frame, effectively raises the automaticity that buildings extracts.
Obviously, it should be appreciated by those skilled in the art, above-mentioned of the present invention each module or each step can realize with general computing system, they can concentrate on single computing system, or be distributed on the network that multiple computing systems form, alternatively, they can be realized with the executable program code of computing system, thereby, they can be stored in storage system and be carried out by computing system.Like this, the present invention is not restricted to any specific hardware and software combination.
Should be understood that, above-mentioned embodiment of the present invention is only for exemplary illustration or explain principle of the present invention, and is not construed as limiting the invention.Therefore any amendment of, making, be equal to replacement, improvement etc., within protection scope of the present invention all should be included in without departing from the spirit and scope of the present invention in the situation that.In addition, claims of the present invention are intended to contain whole variations and the modification in the equivalents that falls into claims scope and border or this scope and border.
Claims (7)
1. a Building Modeling method, is characterized in that, comprising:
Step 1: airborne LIDAR data are carried out to pre-service, be included in cloud data ground point is separated with non-ground point;
Step 2: extract building object point cloud information in described non-ground point;
Step 3: a cloud information is cut apart, obtained buildings object;
Step 4: extract the buildings rule frame in buildings object;
Step 5: dough sheet is pressed in the roof of buildings object and classify;
Step 6: according to described classification results, set up building model;
Step 7: show the modelling effect of setting up.
2. method according to claim 1, is characterized in that, described step 3 utilizes rays method to cut apart a cloud information, obtains the buildings object of separation mutually,
Wherein, in the cutting procedure of rays method, adopt and draw polygon, utilize singularity and the parity of ray and polygon focus, whether judging point is in polygon inside.
3. method according to claim 1, is characterized in that, described step 4 further comprises, based on the process of extracting contour of building line, in extracting contour of building line point, has recorded the order of outline line point,
Described extraction contour of building line further comprises:
Outline line point using the concentrated convex hull summit obtaining as buildings, projects to point set in XOY plane,
Step 4.1: determine first convex hull point of buildings, selected point of the present invention is concentrated the starting point that the point of x coordinate minimum is convex hull, if there is the point that x coordinate is identical, the starting point that the point of getting y coordinate minimum is convex hull,
Step 4.2: in search when next outline line point, left as initial direction, record start direction is put the angle of centrostigma and the line segment being connected of starting point to son taking level, the convex hull point that the point of angle minimum is this sub-point set, is the encirclement shell point of buildings,
Step 4.3: if do not determine sub-point set, expand successively sweep limit, surround shell point until obtain, centered by the point just having obtained, determine sub-point set,
Step 4.4: the line of the encirclement shell point obtaining taking the current encirclement shell point obtaining and last time is as initial direction, determines that son puts concentrated convex hull point, as the point of contour of building line,
Step 4.5: repeating step 4.2-4.4, until obtain all frontier points.
4. method according to claim 3, is characterized in that, described encirclement shell is further by following Procedure Acquisition:
(a) input buildings discrete point cloud data collection S;
(b) set up the Grid Index of discrete point cloud data;
(c) determine starting point, i.e. lower-left point A;
(d) taking starting point as the center of circle, 1.5-2 times of dot spacing is radius, determines current scope Point Set S1={P
1, P
2..., P
ns1={P
1, P
2..., P
n, wherein P
1-p
nfor).
(e) from point set S, determine and surround shell point P_ (B), wherein P_ (B) ∈ S1.
(f), with the current point of P_ (B), repeating step c, d and e, determine a lower P_ (B).
(g) until current some P_ (B) is identical with some A,, outline line point extracts complete, and process finishes.
5. method according to claim 1, is characterized in that, described step 5 further comprises:
Whether exceed setting threshold according to large class point proportion, will isolate wrong class point and be corrected as large class type, to optimize the classification of triangulation network normal vector.
6. method according to claim 1, is characterized in that, described step 6 further comprises:
According to the buildings dough sheet sorting out, each dough sheet is simulated to corresponding plane equation,
According to the buildings regular borders of having obtained, set up between each dough sheet and with the topological relation of the each line segment of regular borders,
Ask the intersection of face and face according to topological relation, the intersection point of line and line,
The unique point that utilization calculates and corresponding topological relation construct building model.
7. for a device for Building Modeling, it is characterized in that, comprising:
Data preprocessing module, for airborne LIDAR data are carried out to pre-service, is included in cloud data ground point is separated with non-ground point;
Point cloud information extraction modules, for extracting building object point cloud information at non-ground point;
Cut apart module, for utilizing rays method to cut apart a cloud information, obtain the buildings object of mutually separating;
Frame extraction module, for extracting the regular frame of buildings;
Roof point sort module, classifies for dough sheet is pressed in roof;
Model building module, for setting up building model according to classification results;
Display module, for showing set up modelling effect at openGL.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410193508.8A CN103969656A (en) | 2014-05-08 | 2014-05-08 | Building modeling method and device based on airborne laser radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410193508.8A CN103969656A (en) | 2014-05-08 | 2014-05-08 | Building modeling method and device based on airborne laser radar |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103969656A true CN103969656A (en) | 2014-08-06 |
Family
ID=51239385
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410193508.8A Pending CN103969656A (en) | 2014-05-08 | 2014-05-08 | Building modeling method and device based on airborne laser radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103969656A (en) |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104898106A (en) * | 2014-12-04 | 2015-09-09 | 武汉天擎空间信息技术有限公司 | Ground point extracting method for complex terrain airborne laser radar data |
| CN106127153A (en) * | 2016-06-24 | 2016-11-16 | 南京林业大学 | The traffic sign recognition methods of Vehicle-borne Laser Scanning cloud data |
| CN106126816A (en) * | 2016-06-24 | 2016-11-16 | 南京林业大学 | Repeat the extensive ALS building point cloud modeling method building under automatic sensing |
| CN106127857A (en) * | 2016-06-24 | 2016-11-16 | 南京林业大学 | Synthetic data drives the on-board LiDAR data modeling method with model-driven |
| CN106199557A (en) * | 2016-06-24 | 2016-12-07 | 南京林业大学 | A kind of airborne laser radar data vegetation extracting method |
| CN106780350A (en) * | 2016-11-30 | 2017-05-31 | 王征 | A kind of 3D model anglec of rotation Self Matching system and methods based on anglec of rotation model library |
| CN107393004A (en) * | 2017-07-17 | 2017-11-24 | 北京数字绿土科技有限公司 | A kind of method and device for obtaining building amount of demolition in power transmission line corridor |
| CN108829951A (en) * | 2018-05-31 | 2018-11-16 | 北京中油瑞飞信息技术有限责任公司 | Reservoir model boundary determining method and device |
| CN109887082A (en) * | 2019-01-22 | 2019-06-14 | 武汉大学 | A kind of interior architecture three-dimensional modeling method and device based on point cloud data |
| CN110648388A (en) * | 2019-08-19 | 2020-01-03 | 北京影谱科技股份有限公司 | Scene geometric modeling method, device and equipment |
| CN110749895A (en) * | 2019-12-23 | 2020-02-04 | 广州赛特智能科技有限公司 | Laser radar point cloud data-based positioning method |
| WO2020103043A1 (en) * | 2018-11-21 | 2020-05-28 | 深圳市大疆创新科技有限公司 | Linear object identification method, device and system and computer storage medium |
| CN111323788A (en) * | 2020-01-19 | 2020-06-23 | 北京建筑大学 | Building change monitoring method and device and computer equipment |
| CN111340136A (en) * | 2020-03-17 | 2020-06-26 | 飞燕航空遥感技术有限公司 | Building point classification expansion method and system in airborne laser radar point cloud |
| CN111815776A (en) * | 2020-02-04 | 2020-10-23 | 山东水利技师学院 | Three-dimensional building fine geometric reconstruction method integrating airborne and vehicle-mounted three-dimensional laser point clouds and streetscape images |
| CN112346409A (en) * | 2020-11-17 | 2021-02-09 | 浙江大学 | Method for rapidly judging point cloud containing problems |
| CN113129323A (en) * | 2021-04-27 | 2021-07-16 | 西安微电子技术研究所 | Remote sensing ridge boundary detection method and system based on artificial intelligence, computer equipment and storage medium |
| CN113345089A (en) * | 2021-05-31 | 2021-09-03 | 西北农林科技大学 | Regularization modeling method based on power tower point cloud |
| WO2022134328A1 (en) * | 2020-12-21 | 2022-06-30 | 福建汇川物联网技术科技股份有限公司 | Construction method and apparatus for building information model |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1200801B1 (en) * | 1999-07-14 | 2004-05-12 | Cyra Technologies, Inc. | Methods for operating a laser scanner |
| CN101604450A (en) * | 2009-07-24 | 2009-12-16 | 武汉大学 | The method of integrated images and LiDAR data extract contour of building |
| CN101702200A (en) * | 2009-11-03 | 2010-05-05 | 武汉大学 | Automatic classification method of airborne laser radar point cloud data |
| CN101950434A (en) * | 2010-09-13 | 2011-01-19 | 天津市星际空间地理信息工程有限公司 | Vehicle-mounted laser infrared radar system and method for automatically measuring urban subassembly |
| CN102521884A (en) * | 2011-12-16 | 2012-06-27 | 南京大学 | Three-dimensional roof reconstruction method based on LiDAR data and ortho images |
| CN103324945A (en) * | 2013-07-08 | 2013-09-25 | 南京大学 | Forest point cloud sorting method based on mode recognition |
-
2014
- 2014-05-08 CN CN201410193508.8A patent/CN103969656A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1200801B1 (en) * | 1999-07-14 | 2004-05-12 | Cyra Technologies, Inc. | Methods for operating a laser scanner |
| CN101604450A (en) * | 2009-07-24 | 2009-12-16 | 武汉大学 | The method of integrated images and LiDAR data extract contour of building |
| CN101702200A (en) * | 2009-11-03 | 2010-05-05 | 武汉大学 | Automatic classification method of airborne laser radar point cloud data |
| CN101950434A (en) * | 2010-09-13 | 2011-01-19 | 天津市星际空间地理信息工程有限公司 | Vehicle-mounted laser infrared radar system and method for automatically measuring urban subassembly |
| CN102521884A (en) * | 2011-12-16 | 2012-06-27 | 南京大学 | Three-dimensional roof reconstruction method based on LiDAR data and ortho images |
| CN103324945A (en) * | 2013-07-08 | 2013-09-25 | 南京大学 | Forest point cloud sorting method based on mode recognition |
Non-Patent Citations (9)
| Title |
|---|
| 吴华意等: ""从机载激光扫描数据中交互式提取人字形房屋模型的方法研究"", 《中国图象图形学报》 * |
| 吴坚等: ""一种检测点是否在多边形或多面体内的方法"", 《小型微型计算机系统》 * |
| 杨斌: ""机载LiDAR点云数据建筑物半自动提取方法研究"", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
| 杨洋等: ""基于LIDAR数据的建筑物自动化重建"", 《测绘科学与技术学报》 * |
| 王红娟: ""判断点在任意简单多边形内的改进算法"", 《福建电脑》 * |
| 符小俐等: ""一种从机载LiDAR点云获取建筑物外部轮廓的方法"", 《测绘工程》 * |
| 胡伟: ""机载LiDAR数据三维建筑物模型重建方法研究"", 《中国优秀硕士学位论文全文数据库基础科学辑》 * |
| 胡伟: ""机载LiDAR数据三维建筑物模型重建方法研究"", 《中国优秀硕士学位论文全文数据库基础科学辑》, no. 1, 15 January 2014 (2014-01-15) * |
| 董秀山等: ""判断点与简单多边形位置关系的新算法"", 《计算机工程与应用》 * |
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104898106A (en) * | 2014-12-04 | 2015-09-09 | 武汉天擎空间信息技术有限公司 | Ground point extracting method for complex terrain airborne laser radar data |
| CN106127153B (en) * | 2016-06-24 | 2019-03-05 | 南京林业大学 | The traffic sign recognition methods of Vehicle-borne Laser Scanning point cloud data |
| CN106126816A (en) * | 2016-06-24 | 2016-11-16 | 南京林业大学 | Repeat the extensive ALS building point cloud modeling method building under automatic sensing |
| CN106127857A (en) * | 2016-06-24 | 2016-11-16 | 南京林业大学 | Synthetic data drives the on-board LiDAR data modeling method with model-driven |
| CN106199557A (en) * | 2016-06-24 | 2016-12-07 | 南京林业大学 | A kind of airborne laser radar data vegetation extracting method |
| CN106127857B (en) * | 2016-06-24 | 2018-11-16 | 南京林业大学 | The on-board LiDAR data modeling method of integrated data driving and model-driven |
| CN106126816B (en) * | 2016-06-24 | 2019-04-05 | 南京林业大学 | Repeat the extensive ALS building point cloud modeling method under building automatic sensing |
| CN106127153A (en) * | 2016-06-24 | 2016-11-16 | 南京林业大学 | The traffic sign recognition methods of Vehicle-borne Laser Scanning cloud data |
| CN106780350A (en) * | 2016-11-30 | 2017-05-31 | 王征 | A kind of 3D model anglec of rotation Self Matching system and methods based on anglec of rotation model library |
| CN107393004A (en) * | 2017-07-17 | 2017-11-24 | 北京数字绿土科技有限公司 | A kind of method and device for obtaining building amount of demolition in power transmission line corridor |
| CN108829951A (en) * | 2018-05-31 | 2018-11-16 | 北京中油瑞飞信息技术有限责任公司 | Reservoir model boundary determining method and device |
| WO2020103043A1 (en) * | 2018-11-21 | 2020-05-28 | 深圳市大疆创新科技有限公司 | Linear object identification method, device and system and computer storage medium |
| CN109887082A (en) * | 2019-01-22 | 2019-06-14 | 武汉大学 | A kind of interior architecture three-dimensional modeling method and device based on point cloud data |
| CN110648388A (en) * | 2019-08-19 | 2020-01-03 | 北京影谱科技股份有限公司 | Scene geometric modeling method, device and equipment |
| CN110749895A (en) * | 2019-12-23 | 2020-02-04 | 广州赛特智能科技有限公司 | Laser radar point cloud data-based positioning method |
| CN111323788A (en) * | 2020-01-19 | 2020-06-23 | 北京建筑大学 | Building change monitoring method and device and computer equipment |
| CN111323788B (en) * | 2020-01-19 | 2022-04-15 | 北京建筑大学 | Building change monitoring method and device and computer equipment |
| CN111815776A (en) * | 2020-02-04 | 2020-10-23 | 山东水利技师学院 | Three-dimensional building fine geometric reconstruction method integrating airborne and vehicle-mounted three-dimensional laser point clouds and streetscape images |
| CN111340136A (en) * | 2020-03-17 | 2020-06-26 | 飞燕航空遥感技术有限公司 | Building point classification expansion method and system in airborne laser radar point cloud |
| CN111340136B (en) * | 2020-03-17 | 2020-10-13 | 飞燕航空遥感技术有限公司 | Building point classification expansion method and system in airborne laser radar point cloud |
| CN112346409A (en) * | 2020-11-17 | 2021-02-09 | 浙江大学 | Method for rapidly judging point cloud containing problems |
| CN112346409B (en) * | 2020-11-17 | 2021-10-08 | 浙江大学 | Method for rapidly judging point cloud containing problems |
| WO2022134328A1 (en) * | 2020-12-21 | 2022-06-30 | 福建汇川物联网技术科技股份有限公司 | Construction method and apparatus for building information model |
| CN113129323A (en) * | 2021-04-27 | 2021-07-16 | 西安微电子技术研究所 | Remote sensing ridge boundary detection method and system based on artificial intelligence, computer equipment and storage medium |
| CN113345089A (en) * | 2021-05-31 | 2021-09-03 | 西北农林科技大学 | Regularization modeling method based on power tower point cloud |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103969656A (en) | Building modeling method and device based on airborne laser radar | |
| CN102521884B (en) | Three-dimensional roof reconstruction method based on LiDAR data and ortho images | |
| Rottensteiner et al. | The ISPRS benchmark on urban object classification and 3D building reconstruction | |
| US7752018B2 (en) | Geospatial modeling system providing building roof type identification features and related methods | |
| Wang et al. | A bayesian approach to building footprint extraction from aerial lidar data | |
| CN104298998A (en) | 3D point cloud data processing method | |
| CN105427377A (en) | Unmanned plane based geological disaster data collection processing method and device | |
| US8099264B2 (en) | Geospatial modeling system providing inpainting and error calculation features and related methods | |
| KR20140014596A (en) | Indoor 3d model extraction method based on point clouds from terrestrial lidar and recording medium thereof | |
| CN109242862A (en) | A kind of real-time digital surface model generation method | |
| US9460520B2 (en) | Method and arrangement for identifying a difference between a first 3D model of an environment and a second 3D model of the environment | |
| CN103093456A (en) | Corn ear character index computing method based on images | |
| US20230260216A1 (en) | Point cloud annotation device, method, and program | |
| Chiabrando et al. | A comparison among different optimization levels in 3D multi-sensor models. A test case in emergency context: 2016 Italian earthquake | |
| CN113537141B (en) | Method and system for rapidly detecting piping and landslide diseases of dykes and dams | |
| Flamanc et al. | 3d city models: an operational approach using aerial images and cadastral maps | |
| CN104657968A (en) | Automatic vehicle-mounted three-dimensional laser point cloud facade classification and outline extraction method | |
| Hu et al. | A fast and simple method of building detection from LiDAR data based on scan line analysis | |
| CN111539100B (en) | Generation method, device, equipment and storage medium of well site virtual construction model | |
| Tutzauer et al. | Semantic urban mesh enhancement utilizing a hybrid model | |
| CN105184854A (en) | Quick modeling method for cloud achievement data of underground space scanning point | |
| Wang et al. | Automatic as-is 3D building models creation from unorganized point clouds | |
| RU2612571C1 (en) | Method and system for recognizing urban facilities | |
| CN102800125B (en) | Large-scale point cloud selection method for supporting lasso | |
| Sarıtaş et al. | Enhancing Ground Point Extraction in Airborne LiDAR Point Cloud Data Using the CSF Filter Algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20140806 |
|
| RJ01 | Rejection of invention patent application after publication |