CN110379022A - Point cloud and grid method of partition in a kind of landform three-dimensional reconstruction system of taking photo by plane - Google Patents
Point cloud and grid method of partition in a kind of landform three-dimensional reconstruction system of taking photo by plane Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
- G06T15/04—Texture mapping
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- 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
Abstract
The invention discloses the point clouds and grid method of partition in a kind of landform three-dimensional reconstruction system of taking photo by plane.This method carries out piecemeal by the three-dimensional data to large scene terrain generation of taking photo by plane, and solves the problems, such as to cause the important modules such as three-dimensional reconstruction system midpoint cloud denseization, triangle gridding and texture mapping that can not handle greatly because of point cloud data amount.This method stage by stage successively divides three-dimensional data according to memory service condition in module, first carries out piecemeal to cloud before cloud denseization and triangle gridding, then carry out piecemeal to triangle gridding before texture mapping processing.The present invention not only reduces the hardware threshold of three-dimensional reconstruction system operation, and can retain well in the block after piecemeal and relationship between block, solid foundation has been established to generate the high-resolution digital orthoimage of large scene (DOM) and numerical cutting tool (DSM).
Description
Technical field
The invention belongs to unmanned plane data application field, in particular in a kind of landform three-dimensional reconstruction system of taking photo by plane
Point cloud and grid method of partition.
Background technique
Stringent control with country to unmanned plane airspace, the safety and stability of industrial unmanned plane, which has, substantially to be mentioned
It rises.It immediately comes the industrial scale applications based on unmanned plane to emerge one after another, wherein the most prominent with take photo by plane Scan Specialty and industry inspection class
Out.Demand of these applications all with large scene 2D map splicing and 3D terrain reconstruction, but since there are images point for the intermediate item
The features such as resolution height and big amount of images, therefore realize that scene rebuilding not only has more dependences to the hardware performance of PC platform, and
And requirements at the higher level also proposed to the algorithm robustness of three-dimensional reconstruction system.
Therefore, further boosting algorithm robustness and hardware can be reduced under the premise of realizing large scene terrain reconstruction
It can require just to be particularly important.Existing method take photo by plane large scene 2D map splicing and 3D terrain reconstruction in because data volume is excessive
Lead to not the problem of normally generating DOM and DSM.
Summary of the invention
The purpose of the present invention is to provide the point cloud and grid method of partition in a kind of landform three-dimensional reconstruction system of taking photo by plane, with
It solves the above problems.
To achieve the above object, the invention adopts the following technical scheme:
Point cloud and grid method of partition in a kind of landform three-dimensional reconstruction system of taking photo by plane, comprising the following steps:
Step 1: estimating phase in such a way that SLAM SfM technology is by nonlinear optimization in three-dimensional reconstruction system
Sparse cloud of seat in the plane appearance and scene carries out piecemeal processing to sparse cloud, does denseization operation to sparse cloud after piecemeal；
Step 2: after cloud denseization, to the processing of dense point cloud piecemeal, after dense point cloud piecemeal, to dense point
Cloud carries out triangle gridding operation；
Step 3: after obtaining triangle gridding, piecemeal being carried out to triangle gridding before texture mapping processing, realizes that generation is big
The demand of resolution ratio DOM and DSM image.
Further, in step 1, the size of sparse cloud piecemeal is indicated by following formula:
Bsparse=f (Nview,Gdense,Npoint,Cram)
Wherein, BsparseIndicate the size of sparse cloud piecemeal, NviewIndicate the quantity of view, GdenseIndicate denseization
Grade, NpointIndicate the quantity of point cloud, CramIndicate the remaining space of memory.
Further, the implementation process of step 1 are as follows: first pass through CramAnd GdenseDetermine the patient view of current memory institute
Amount threshold and point cloud amount threshold, move towards then along the course line of views taken, accumulate NviewAnd NpointQuantity, until looking for
To meeting under threshold condition until maximum piecemeal.
Further, in step 2, the size of dense point cloud piecemeal is indicated by following formula:
Bdense=f (Npoint,Cram)
Wherein, BdenseIndicate the size of dense point cloud piecemeal, NpointIndicate the quantity of point cloud, CramIndicate the residue of memory
Space.
Further, the implementation process of step 2 are as follows: determine the threshold of point cloud quantity according to the size of memory remaining space first
Value is moved towards then along the course line of views taken, accumulates NpointQuantity, meet maximum piecemeal under threshold condition until finding
Until.
Further, in step 3, the relationship of triangle gridding piecemeal is indicated with following formula:
Bmesh=f (Rdom/dsm, Cram)
Wherein, BmeshIndicate the size of triangle gridding piecemeal, Rdom/dsmIndicate the resolution ratio of DOM and DSM, CramIndicate memory
Remaining space.
Further, in step 3, after the size for obtaining grid piecemeal, grid is existed according to fixed change in coordinate axis direction
Cutting is carried out on horizontal plane, generates mutually independent triangle gridding block；It needs during grid cutting in grid
Vertex, dough sheet and view split, deleted, re-establish index relative operation, top is avoided the occurrence of during operation
Point rejects the phenomenon that unclean or dough sheet is lost；It should ensure that there are certain degrees of overlapping between adjacent mesh in grid piecemeal.
Compared with prior art, the present invention has following technical effect:
The present invention using the point cloud that generates as being originally inputted, on the basis of old process in joined three deblockings
The process of processing is the process of the process of sparse cloud piecemeal, the process of dense point cloud piecemeal and triangle gridding piecemeal respectively.It is logical
Crossing method proposed by the invention may be implemented layer-by-layer piecemeal, to ensure flexibly realize big field on different hardware platforms
The processing of scape data, reduces pooled error to greatest extent.
The present invention is by solving the layer-by-layer piecemeal of the data of large scene three-dimensional reconstruction because platform resource deficiency is to algorithm Shandong
Stick bring influences, and finally realizes the demand for generating big resolution ratio DOM and DSM image.
Present invention reduces the hardware threshold of three-dimensional reconstruction system operation, in block after piecemeal can be retained well and block
Between relationship, established solid base to generate the high-resolution digital orthoimage of large scene (DOM) and numerical cutting tool (DSM)
Plinth.
Detailed description of the invention
Fig. 1 is the flow chart of method of partition proposed by the invention in three-dimensional reconstruction system；
Fig. 2 is the schematic diagram of the midpoint step S1 and S2 cloud partition strategy in the present invention；
Fig. 3 is the schematic diagram of step S3 intermediate cam grid partition strategy in the present invention；
Fig. 4 be in the present invention after the completion of step S3 grid piecemeal between adjacent block overlapping region schematic diagram；
Fig. 5 is utilization exemplary diagram of the present invention in pipeline inspection project.
Specific embodiment
Below in conjunction with attached drawing, the present invention is further described:
Please refer to Fig. 1 to Fig. 5, the point cloud and grid method of partition in a kind of landform three-dimensional reconstruction system of taking photo by plane, including with
Lower step:
Step 1: estimating phase in such a way that SLAM SfM technology is by nonlinear optimization in three-dimensional reconstruction system
Sparse cloud of seat in the plane appearance and scene carries out piecemeal processing to sparse cloud, does denseization operation to sparse cloud after piecemeal；
Step 2: after cloud denseization, to the processing of dense point cloud piecemeal, after dense point cloud piecemeal, to dense point
Cloud carries out triangle gridding operation；
Step 3: after obtaining triangle gridding, piecemeal being carried out to triangle gridding before texture mapping processing, realizes that generation is big
The demand of resolution ratio DOM and DSM image.
In step 1, the size of sparse cloud piecemeal is indicated by following formula:
Bsparse=f (Nview,Gdense,Npoint,Cram)
Wherein, BsparseIndicate the size of sparse cloud piecemeal, NviewIndicate the quantity of view, GdenseIndicate denseization
Grade, NpointIndicate the quantity of point cloud, CramIndicate the remaining space of memory.
The implementation process of step 1 are as follows: first pass through CramAnd GdenseDetermine the current memory patient amount of views threshold value of institute and
Point cloud amount threshold is moved towards then along the course line of views taken, accumulates NviewAnd NpointQuantity, meet threshold value until finding
In the case of until maximum piecemeal.
In step 2, the size of dense point cloud piecemeal is indicated by following formula:
Bdense=f (Npoint,Cram)
Wherein, BdenseIndicate the size of dense point cloud piecemeal, NpointIndicate the quantity of point cloud, CramIndicate the residue of memory
Space.
The implementation process of step 2 are as follows: determine the threshold value of point cloud quantity according to the size of memory remaining space first, then edge
Views taken course line trend, accumulate NpointQuantity, until meeting maximum piecemeal under threshold condition until finding.
In step 3, the relationship of triangle gridding piecemeal is indicated with following formula:
Bmesh=f (Rdom/dsm,Cram)
Wherein, BmeshIndicate the size of triangle gridding piecemeal, Rdom/dsmIndicate the resolution ratio of DOM and DSM, CramIndicate memory
Remaining space.
In step 3, after the size for obtaining grid piecemeal, according to fixed change in coordinate axis direction by grid in horizontal plane
Upper carry out cutting, generates mutually independent triangle gridding block；It needs during grid cutting to the vertex in grid, dough sheet
And view is split, is deleted, re-establishing index relative operation, and it is unclean that vertex rejecting is avoided the occurrence of during operation
Or the phenomenon that dough sheet loss；It should ensure that there are certain degrees of overlapping between adjacent mesh in grid piecemeal.
Three-dimensional reconstruction system is generally divided into cloud and generates and model construction two parts, point cloud generation refer to using SLAM or
SfM technology estimates sparse cloud of camera pose and scene by way of nonlinear optimization, and model construction is sparse
DOM and DSM is generated by main modulars such as point cloud denseization, triangle gridding and texture mapping on the basis of point cloud.When a cloud
When data volume is excessive, point cloud denseization grade is excessively high or the resolution ratio of texture mapping is excessive, the operation of above three module
Pressure will all increase, wherein most commonly seen with interruption caused by low memory.Point cloud and grid piecemeal side proposed by the invention
Method is exactly in order to solve this problem, using the point cloud of generation as being originally inputted, to be handled mainly for model construction part,
Process as shown in Figure 1: on the basis of old process in joined three deblockings processing process, be sparse cloud respectively
The S3 process of the S1 process of piecemeal, the S2 process of dense point cloud piecemeal and triangle gridding piecemeal.The side proposed through the invention
Layer-by-layer piecemeal may be implemented in method, maximum to ensure flexibly realize the processing of large scene data on different hardware platforms
The reduction pooled error of limit.
The specific implementation step of the method is as follows:
Step S1:
When three-dimensional reconstruction, after obtaining sparse cloud, richer details, needs to do sparse cloud dense in order to obtain
Change operation.In cloud denseization module, all views that can be covered first to sparse cloud solve depth map respectively, these
The occupied memory size of depth map is related with denseization grade, and denseization higher grade, and shared memory is bigger；Then will own
The depth map of view is fused into a complete depth map.In this process, figure closeness, denseization grade and point cloud cover
The factors such as lid range can all cause different degrees of pressure to hardware memory, and may cause because of low memory algorithm collapse,
The problems such as process stops.Consider above-mentioned influence factor, the size of sparse cloud piecemeal can be indicated by formula 1:
Bsparse=f (Nview,Gdense,Npoint,Cram) (formula 1)
Wherein, BsparseIndicate the size of sparse cloud piecemeal, NviewIndicate the quantity of view, GdenseIndicate denseization
Grade, NpointIndicate the quantity of point cloud, CramIndicate the remaining space of memory.The implementation process of this method is to first pass through CramWith
GdenseIt determines the patient amount of views threshold value of current memory institute and point cloud amount threshold, is walked then along the course line of views taken
To accumulation NviewAnd NpointQuantity, until meeting maximum piecemeal under threshold condition until finding.
Compared to amount of views piecemeal and space length both common methods of piecemeal at equal intervals at equal intervals, methods herein
With higher robustness.Piecemeal can be influenced by view degree of overlapping and be difficult to comprehensively consider number of views amount of views at equal intervals
Relationship between amount and point cloud；Piecemeal can generate more space length in the complicated scene of processing such as course line variation at equal intervals
More crumb datas simultaneously causes bigger pooled error.
The partition strategy of sparse cloud is as shown in Fig. 2: according to sparse cloud method of partition proposed in this paper along course line
The trend point cloud that is covered view in region carry out cutting, due between view and view there are certain degree of overlapping, because
There can be certain overlapping point cloud between this adjacent block, these overlapping point clouds are the guarantees that the relationship that merges is established between adjacent block.
Step S2:
After cloud denseization, the quantity for putting cloud can be increased dramatically, and the point cloud after denseization is known as dense point cloud.?
The process that tri patch relationship is established between point cloud is known as triangle gridding, and triangle gridding will be deleted point extra in a cloud simultaneously
Smooth treatment is carried out to obtain ideal surface to the grid of generation.It will use figure during triangle gridding to cut to grid progress
Optimization, which can consume a large amount of memory, the problem of as will appear low memory when fruit dot cloud quantity is excessive, dense point cloud point
The purpose of block is exactly in order to solve this problem.
It is cut by figure during solving optimal surface, the quantity for putting cloud is directly related to the size of figure.The quantity of point cloud
Bigger, the scale of construction and complexity of figure are also higher, and the memory of consumption is also bigger.It therefore, can be by the size of dense point cloud piecemeal
It is indicated by formula 2:
Bdense=f (Npoint,Cram) (formula 2)
Wherein, BdenseIndicate the size of dense point cloud piecemeal, NpointIndicate the quantity of point cloud, CramIndicate the residue of memory
Space.The implementation process of this step is similar with step S1, determines the threshold of point cloud quantity according to the size of memory remaining space first
Value is moved towards then along the course line of views taken, accumulates NpointQuantity, meet maximum piecemeal under threshold condition until finding
Until.
Step S3:
After obtaining triangle gridding, the DSM of scene can be generated according to the shape of tri patch；Can also according to vertex with
Relationship between view searches out the corresponding optimal texture patch of each tri patch, and ultimately generates in scene with textured letter
The DOM of breath.Require the memory headroom of one piece of storage image data of application during DSM and DOM are generated, the space it is big
Small and sizing grid and image resolution ratio direct proportionality, and the data volume of grid is excessive or the picture mosaic generated is required to differentiate
Rate is excessively high all to cause memory application to fail.It therefore, can be with as the foundation of grid piecemeal using free memory and picture mosaic resolution ratio
Solve the problems, such as this, the relationship of triangle gridding piecemeal can be indicated with formula 3:
Bmesh=f (Rdom/dsm,Cram) (formula 3)
Wherein, BmeshIndicate the size of triangle gridding piecemeal, Rdom/dsmIndicate the resolution ratio of DOM and DSM, CramIndicate memory
Remaining space.The partition strategy of grid and the partition strategy for putting cloud are different, the occupied memory headroom of image and image
Elemental area it is related, can apply to guarantee each piecemeal during generating DOM and DSM to enough memory skies
Between, area of the piecemeal on horizontal plane should be constrained, and the size of the area determines the size of piecemeal, mainly by Cram
And Rdom/dsmThe two factors determine.The strategy of triangle gridding piecemeal is as shown in Fig. 3, after the size for obtaining grid piecemeal,
According to fixed change in coordinate axis direction grid is subjected to cutting on horizontal plane, generates mutually independent triangle gridding block.
It needs to split vertex, dough sheet and the view in grid during grid cutting, deletes, re-establishes
The operation such as index relative should be avoided during operation and the phenomenon that unclean or dough sheet loss is rejected on vertex occurs.In addition, being
Avoid generating error in subsequent DOM and DSM image merging process, should ensure that in grid piecemeal between adjacent mesh exist it is certain
Degree of overlapping, as shown in Fig. 4, figure a, edge of the figure b for two adjacent grid blocks between, scheming the high bright part in c is two nets
The tri patch region being overlapped between lattice block.
In short, the layer-by-layer piecemeal of the data of large scene three-dimensional reconstruction can be solved because of platform by three above step
Inadequate resource influences algorithm robustness bring, and finally realizes the demand for generating big resolution ratio DOM and DSM image.
Fig. 5 is that the pipe string of 20km long is adopted in the utilization of method proposed by the invention in pipeline inspection project
Collect the image that 658 resolution ratio are 6000 × 4000, may be implemented in the full resolution on low configuration platform with the partition strategy
Rate image mosaic.
Claims (7)
1. point cloud and grid method of partition in a kind of landform three-dimensional reconstruction system of taking photo by plane, which comprises the following steps:
Step 1: estimating phase seat in the plane in such a way that SLAM SfM technology is by nonlinear optimization in three-dimensional reconstruction system
Sparse cloud of appearance and scene carries out piecemeal processing to sparse cloud, does denseization operation to sparse cloud after piecemeal；
Step 2: after cloud denseization, to the processing of dense point cloud piecemeal, after dense point cloud piecemeal, to dense point cloud into
The operation of row triangle gridding；
Step 3: after obtaining triangle gridding, piecemeal being carried out to triangle gridding before texture mapping processing, realizes and generates big differentiate
The demand of rate DOM and DSM image.
2. point cloud and grid method of partition in a kind of landform three-dimensional reconstruction system of taking photo by plane according to claim 1, special
Sign is, in step 1, the size of sparse cloud piecemeal is indicated by following formula:
Bsparse=f (Nview,Gdense,Npoint,Cram)
Wherein, BsparseIndicate the size of sparse cloud piecemeal, NviewIndicate the quantity of view, GdenseIndicate the grade of denseization,
NpointIndicate the quantity of point cloud, CramIndicate the remaining space of memory.
3. point cloud and grid method of partition in a kind of landform three-dimensional reconstruction system of taking photo by plane according to claim 2, special
Sign is, the implementation process of step 1 are as follows: first pass through CramAnd GdenseDetermine the current memory patient amount of views threshold value of institute and
Point cloud amount threshold is moved towards then along the course line of views taken, accumulates NviewAnd NpointQuantity, meet threshold value until finding
In the case of until maximum piecemeal.
4. point cloud and grid method of partition in a kind of landform three-dimensional reconstruction system of taking photo by plane according to claim 1, special
Sign is, in step 2, the size of dense point cloud piecemeal is indicated by following formula:
Bdense=f (Npoint,Cram)
Wherein, BdenseIndicate the size of dense point cloud piecemeal, NpointIndicate the quantity of point cloud, CramIndicate the remaining space of memory.
5. point cloud and grid method of partition in a kind of landform three-dimensional reconstruction system of taking photo by plane according to claim 4, special
Sign is, the implementation process of step 2 are as follows: determines the threshold value of point cloud quantity according to the size of memory remaining space first, then edge
Views taken course line trend, accumulate NpointQuantity, until meeting maximum piecemeal under threshold condition until finding.
6. point cloud and grid method of partition in a kind of landform three-dimensional reconstruction system of taking photo by plane according to claim 1, special
Sign is, in step 3, the relationship of triangle gridding piecemeal is indicated with following formula:
Bmesh=f (Rdom/dsm,Cram)
Wherein, BmeshIndicate the size of triangle gridding piecemeal, Rdom/dsmIndicate the resolution ratio of DOM and DSM, CramIndicate the surplus of memory
Complementary space.
7. point cloud and grid method of partition in a kind of landform three-dimensional reconstruction system of taking photo by plane according to claim 6, special
Sign is, in step 3, after the size for obtaining grid piecemeal, according to fixed change in coordinate axis direction by grid in horizontal plane
Upper carry out cutting, generates mutually independent triangle gridding block；It needs during grid cutting to the vertex in grid, dough sheet
And view is split, is deleted, re-establishing index relative operation, and it is unclean that vertex rejecting is avoided the occurrence of during operation
Or the phenomenon that dough sheet loss；It should ensure that there are certain degrees of overlapping between adjacent mesh in grid piecemeal.
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Application publication date: 20191025 |