CN106097436B - A kind of three-dimensional rebuilding method of large scene object - Google Patents
A kind of three-dimensional rebuilding method of large scene object Download PDFInfo
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Abstract
A kind of three-dimensional rebuilding method of large scene object, it is characterised in that: the following steps are included: 1) with camera with different view, position several sequence photographs are acquired to scenario objects；2) sequence photographs, are divided into several unit Ni, 3), feature detection, matching carried out using three sequences piece of the SIFT algorithm to each unit；4) by unit N1, first sequence photographs initialization of (i=1), basis matrix F, the essential matrix E of other two sequence photographs of calculating；5) the camera parameter R of every sequence photographs in other units, is solvedi、TiWith three-dimensional point cloud coordinate Mj)；7), the extension of seed dough sheet；8), the filtering of seed dough sheet；The present invention obtains accurate basis matrix using the effective removal Mismatching point of three-view diagram constraint, the precision of reconstruction is high, and coordinate conversion is not needed between unit, computation complexity is reduced, it is denser by carrying out the point cloud that dense reconstruction makes to obtain threedimensional model on the basis of exercise recovery structure.
Description
Technical field
The present invention relates to a kind of computer vision and field of Computer Graphics, especially a kind of three-dimensional of large scene object
Method for reconstructing.
Background technique
The final purpose of three-dimensional reconstruction is to restore the three-dimensional structure of target scene, and the three-dimensional reconstruction based on image sequence is to obtain
Take one of the main means of three-dimensional structure.This method can be regarded as the inverse process of photograph, advantage of lower cost, it is only necessary to general
Logical video camera, it is easy to operate, easy to carry, be by the Feature Points Matching of image sequence and multiple view stereo reconstruction based on
Research foundation and emphasis in calculation machine vision technique.
The present more method of three-dimensional rebuilding method for large scene object is SFM (Structure From
Motion), the disadvantage is that feature detection is constrained with when matching using Epipolar geometry, match point can only be determined in corresponding polar curve
On, without can determine that match point accurate location, this constraint is weaker.Once Mismatching point deletion is not fallen, basis matrix is solved
Mistake, obtained three-dimensional point cloud coordinate are also mistake, and the corresponding cloud of other images match points acquired with this is sat
Mark is also mistake, with regard to will appear error accumulation problem.Also one is the three-dimensional reconstructions based on independent three-view diagram, every three width
Figure carries out three-dimensional reconstruction as a separate unit, is coordinately transformed between each unit after reconstruction, transformation of scale is all
Three-dimensional point cloud be transformed under the same coordinate, this method computation complexity is bigger.In addition both methods have one it is common
The problem of be rebuild point cloud all than sparse, be not able to satisfy visualization effect.It also needs to carry out after general sparse reconstruction
Dense reconstruction, the most of dense method for reconstructing are three-dimensional reconstruction (the Patched-based Multi-View based on dough sheet
Stereo, PMVS).The input of PMVS method be one group of image arrangement set and it is sparse rebuild camera parameter R, T for acquiring, so
The point off density cloud of threedimensional model is exported afterwards.
Summary of the invention
In view of the deficiencies of the prior art, the present invention provides a kind of three-dimensional rebuilding method of large scene object.
The technical solution of the present invention is as follows: a kind of three-dimensional rebuilding method of large scene object, it is characterised in that: including following step
It is rapid:
1) with camera with different view, position several sequence photographs are acquired to scenario objects, and according to the successive of acquisition
Sequence numbers several sequence photographs for Ik, k=1,2,3...N；
2) sequence photographs are divided into several unit N according to the sequencing of numberi, i=1,2,3...n, each unit
Including three sequence photographs successively acquired, and by unit Ni, i=1,2,3...n third sequence photographs are as under adjacent
One unit Ni+1, i=1,2,3...n-1 first sequence photographs, and using the sequence photographs as the public of two adjacent cells
Sequence photographs；
3) by using SIFT algorithm to each unit Ni, i=1,2, the 3...n three sequence photographs progress features for including
Detection, matching, and utilize three-view diagram constraint removal Mismatching point；
4) by unit Ni, the camera parameter initialization of first sequence photographs of i=1, and found out using RANSANC algorithm
Unit Ni, basis matrix F, the essential matrix E of other two sequence photographs in i=1 obtain every in other two sequence photographs
Open the camera parameter R of sequence photographsh、TgWith three-dimensional point cloud coordinate Mj；
5) by unit N in adjacent Unit twoi, i=1, the camera parameter R of common sequence photo in 2,3...nh、TgAs it
Adjacent next unit Ni+1, i=1, the initial value of the camera parameter of 2,3...n-1 common sequence photo, and solve its it is adjacent under
One unit Ni+1, i=1, the camera parameter R of other two sequence photographs in 2,3...n-1h、TgWith three-dimensional point cloud coordinate Mj, with this
Calculate unit Ni+1, i=1, the camera parameter R of every sequence photographs in 2,3...n-1 other two sequence photographsh、TgWith three
Dimension point cloud coordinate Mj；
6) by the camera parameter R of all sequence photographsi、TiWith three-dimensional point cloud coordinate MjAs the input of PMVS algorithm, from
And obtain the seed dough sheet of scenario objects；
7) extension of seed dough sheet: by sequence photographs IkIt is divided into several image blocks Co(x, y) utilizes neighboring seeds face
There is piece position and the similar characteristic of normal direction to be extended to seed dough sheet, so that the seed dough sheet after extension be made gradually to cover mesh
Mark the surface of object；
8) seed dough sheet filters: it is wrong that extension is rejected using local gray level consistency constraint and globally visible consistency constraint
Seed dough sheet accidentally.
In above-mentioned technical proposal, when camera surrounds scene objects acquisition sequence photo in step 1), adjacent two sequences are shone
Angle between piece is 5 °~15 °, and the sequence photographs quantity of acquisition is odd number, and the sequence photographs of acquisition are at least 3.
In above-mentioned technical proposal, the sequence photographs acquired in step 1) are 45.
In above-mentioned technical proposal, feature detection, matching are carried out to sequence photographs using SIFT algorithm in step 3), to count
Calculate the corresponding image points m of three sequence photographs in each unitkj, wherein the corresponding image points of any two sequence photographs is at another
Match point on sequence photographs is on the intersection point that any two sequences piece corresponding image points corresponds to polar curve, if match point and intersection point
Distance be more than that two pixels are then used as Mismatching point to delete.
Unit N in above-mentioned technical proposal, in step 4) after initializationi, the camera ginseng of first sequence photographs of i=1
Number R1For 3 × 3 unit matrix, T1For 3 × 1 null matrix；
Basis matrix F is solved using RANSANC algorithm, combining camera internal reference K solves essential matrix E, then to essential square
Battle array E carries out singular value decomposition and obtains camera parameter Rh、Tg, and obtain three-dimensional point cloud coordinate Mj, then adjusted using boundling
The camera parameter R of (Bundle Adjustment) algorithm local optimum sequence photographsh、Tg, three-dimensional point cloud coordinate Mj, pass through boundling
Adjustment (bundle adjustment) algorithm makes three-dimensional point cloud coordinate MjRe-projection point in the of the same name of kth sequence photographs
Picture point mkiThe quadratic sum of difference is minimum, calculating formula are as follows:
Wherein D is Euclidean distance, and N is sequence photographs number, and M is three-dimensional point cloud number of coordinates.
In above-mentioned technical proposal, the extension in step 7) by seed dough sheet in each image block so that at least reconstruct
One seed dough sheet.
In above-mentioned technical proposal, the seed dough sheet of extended error includes external seed dough sheet and internal seeds face in step 8)
Piece by the extension of seed dough sheet, is completely covered target object surface by dense seed dough sheet filtration cycle iteration 3 times.
The invention has the benefit that can effectively remove Mismatching point using three-view diagram constraint obtains accurate basis
Matrix, the precision of reconstruction is high, and coordinate conversion is not needed between unit, thus greatly reduces computation complexity, by
It is denser that the point cloud that dense reconstruction makes to obtain threedimensional model is carried out on the basis of exercise recovery structure.
Detailed description of the invention
Fig. 1 is that three-view diagram of the invention constrains schematic diagram；
Specific embodiment
Specific embodiments of the present invention will be further explained with reference to the accompanying drawing:
As shown in Figure 1, a kind of three-dimensional rebuilding method of large scene object, it is characterised in that: the following steps are included:
1) with camera with different view, position several sequence photographs are acquired to scenario objects, and according to the successive of acquisition
Sequence numbers several sequence photographs for Ik, k=1,2,3...N；
2) sequence photographs are divided into several unit N according to the sequencing of numberi, (i=1,2,3...n), Mei Gedan
Member includes three sequence photographs successively acquiring, and by unit Ni, the third sequence photographs of (i=1,2,3...n) are as phase
Adjacent next unit Ni+1, i=1,2,3...n-1 first sequence photographs, and using the sequence photographs as two adjacent cells
Common sequence photo；
3) by using SIFT algorithm to each unit Ni, three sequences piece that (i=1,2,3...n) includes carries out special
Sign detection, matching, and utilize three-view diagram constraint removal Mismatching point；
4) by unit Ni, the camera parameter initialization of first sequence photographs of (i=1), and asked using RANSANC algorithm
Unit N outi, basis matrix F, the essential matrix E of other two sequence photographs in (i=1) obtain in two sequence photographs every
Open the camera parameter R of sequence photographsh、TgWith three-dimensional point cloud coordinate Mj；
5) a unit N oni, the camera parameter R of common sequence photo in (i=1,2,3...n)h、TgAs next unit
Ni+1, i=1,2,3...n-1 first open the initial value of the camera parameter of sequence photographs, and solve next unit Ni+1, i=1,2,
The camera parameter R of two sequence photographs of other in 3...n-1h、TgWith three-dimensional point cloud coordinate Mj, unit N is calculated with thisi+1, i=
The camera parameter R of every sequence photographs in 1,2,3...n-1 other two sequence photographsh、TgWith three-dimensional point cloud coordinate Mj；
6) by the camera parameter R of all sequence photographsi、TiWith three-dimensional point cloud coordinate MjAs the input of PMVS algorithm, from
And obtain the seed dough sheet of scenario objects；
7) extension of seed dough sheet: by sequence photographs IkIt is divided into several image blocks Co(x, y) utilizes neighboring seeds face
There is piece position and the similar characteristic of normal direction to be extended to seed dough sheet, so that the seed dough sheet after extension be made gradually to cover mesh
Mark the surface of object；
8) seed dough sheet filters: it is wrong that extension is rejected using local gray level consistency constraint and globally visible consistency constraint
Seed dough sheet accidentally.
In above-mentioned technical proposal, when camera surrounds scene objects acquisition sequence photo in step 1), adjacent two sequences are shone
Angle between piece is 5 °~15 °, and the sequence photographs quantity of acquisition is odd number, and the sequence photographs of acquisition are at least 3.
In above-mentioned technical proposal, the sequence photographs acquired in step 1) are 45.
In above-mentioned technical proposal, feature detection, matching are carried out to sequence photographs using SIFT algorithm in step 3), to count
Calculate the corresponding image points m of three sequence photographs in each unitkj, wherein the corresponding image points of any two sequence photographs is at another
Match point on sequence photographs is on the intersection point that any two sequences piece corresponding image points corresponds to polar curve, if match point and intersection point
Distance be more than that two pixels are then used as Mismatching point to delete, x, x ', x in Fig. 1 " be any unit three open the of the same name of sequence photographs
Picture point, wherein the corresponding image points x ' of two sequence photographs, x " match point put two sequence photographs corresponding image points x ', the x " are right
The polar curve l answered12、l13Intersection point on, deleted if more than 2 pixels at a distance from match point with this intersection point as Mismatching point
It removes.
Unit N in above-mentioned technical proposal, in step 4) after initializationi, the camera of first sequence photographs of (i=1)
Parameter R1For 3 × 3 unit matrix, T1For 3 × 1 null matrix；
Basis matrix F is solved using RANSANC algorithm, combining camera internal reference K solves essential matrix E, then to essential square
Battle array E carries out singular value decomposition and obtains camera parameter Rh、Tg, and obtain three-dimensional point cloud coordinate Mj, then adjusted using boundling
The camera parameter R of (Bundle Adjustment) algorithm local optimum sequence photographsh、Tg, three-dimensional point cloud coordinate Mj, pass through boundling
Adjustment (bundle adjustment) algorithm makes three-dimensional point cloud coordinate MjRe-projection point in the of the same name of kth sequence photographs
Picture point mkjThe quadratic sum of difference is minimum, calculating formula are as follows:
Wherein D is Euclidean distance, and N is sequence photographs number, and M is three-dimensional point cloud number of coordinates.
In above-mentioned technical proposal, using the characteristic point of Harris and DOG operator detection sequence photo in step 6), find full
The potential matching double points of sufficient Epipolar geometry constraint acquire three-dimensional point cloud coordinate using triangle Principle Method to potential matching double points
Mi, seed dough sheet center c (p) is three-dimensional point cloud coordinate Mj, sequence photographs collection is combined into Im={ Ik| k=1,2,3 ..., N }, space
The unit normal vector n (p) of dough sheet p is the camera photocentre that reference image R (p) is directed toward by space dough sheet center c (p), it may be assumed that
c(p)←{Triangulation from f and f′}
R(p)←Ik
V (p), V are initialized simultaneously*(p), visual picture collection subject to V (p), the unit of the space dough sheet p of V (p) sequence photographs
Angle where normal vector n (p) and space dough sheet center c (p) between light is less than 60 °, it may be assumed that
Wherein O (Ik) it is sequence photographs IkThe optical center of corresponding camera,
V*It (p) is visual picture collection, V*(p) the space dough sheet center c (p) of the sequence photographs in projects to reference image R
(p) value of the gray consistency function between the sequence photographs is greater than threshold alpha=0.4, it may be assumed that
V*(p)=I | I ∈ V (p), h (p, I, R (p)) > α },
Wherein h (p, I, R (p)) refers to the gray scale uniform metric between sequence photographs I and reference image R (p), is taken as 1 and subtracts
Its NCC (normalization intersection correlation) value is gone, then with space dough sheet p in set V*(p) the gray consistency function on
Come advanced optimize space dough sheet center c (p) and
The unit normal vector n (p) of space dough sheet, with the unit normal vector n (p) of seed dough sheet center c (p) after optimization and seed dough sheet come
Update V (p), the V of dough sheet*(p), if | V*(p) | > γ then generates the success of seed dough sheet, then by every sequence photographs IkIt draws
It is divided into the image block C that size is β × β (β=32)o(x, y), arbitrary image block Co(x, y) there are two corresponding set Qi(x, y),
Qi *(x, y) is used to store the corresponding real space dough sheet of the image block, wherein x, y are sequence photographs IiIndex；Then life
At seed dough sheet project on the sequence photographs of division, record seed dough sheet where image block Co(x, y),
In above-mentioned technical proposal, the extension in step 7) by seed dough sheet p in each image block so that at least rebuild
A seed dough sheet out,
In above-mentioned technical proposal, the seed dough sheet p of extended error includes external seed dough sheet and internal seeds in step 8)
Dough sheet by the extension of seed dough sheet, is completely covered target object surface by dense seed dough sheet filtration cycle iteration 3 times.
The above embodiments and description only illustrate the principle of the present invention and most preferred embodiment, is not departing from this
Under the premise of spirit and range, various changes and improvements may be made to the invention, these changes and improvements both fall within requirement and protect
In the scope of the invention of shield.
Claims (5)
1. a kind of three-dimensional rebuilding method of large scene object, it is characterised in that: the following steps are included:
1) by camera with different view, position several sequence photographs are acquired to scenario objects, and according to the successive suitable of acquisition
Sequence numbers several sequence photographs for Ik, k=1,2,3...N；
2) sequence photographs are divided into several unit N according to the sequencing of numberi, i=1,2,3...n, each unit packet
Include three sequence photographs successively acquired, and by unit NiThird sequence photographs as adjacent next unit Ni+1First
Sequence photographs are opened, and using the sequence photographs as the common sequence photo of two adjacent cells；
3) by using SIFT algorithm to each unit Ni, i=1,2,3...n three sequences for including piece progress feature detection,
Matching, and Mismatching point is removed using three-view diagram constraint, specifically:
Feature detection, matching are carried out to sequence photographs by SIFT algorithm, to calculate three sequence photographs in each unit
Corresponding image points mij, wherein match point of the corresponding image points of any two sequence photographs on another sequence photographs is any at this
Two sequences piece corresponding image points correspond on the intersection point of polar curve, the conduct mistake if more than two pixels at a distance from the match point with intersection point
Match point deletion；
4) by unit Ni, the camera parameter initialization of first sequence photographs of i=1, and unit is found out using RANSANC algorithm
Ni, basis matrix F, the essential matrix E of other two sequence photographs in i=1 obtain every sequence in two sequence photographs and shine
The camera parameter R of pieceh、TgWith three-dimensional point cloud coordinate Mj, specifically:
Using RANSANC algorithm solve basis matrix F, combining camera internal reference K solve essential matrix E, then to essential matrix E into
Row singular value decomposition obtains camera parameter Rh、Tg, and obtain three-dimensional point cloud coordinate Mj, then locally excellent using boundling adjustment algorithm
Change the camera parameter R of sequence photographsh、Tg, three-dimensional point cloud coordinate Mj, three-dimensional point cloud coordinate M is made by boundling adjustment algorithmjWeight
Subpoint and the corresponding image points m in kth sequence photographskiThe quadratic sum of difference is minimum, calculating formula are as follows:
Wherein D is Euclidean distance, and N is sequence photographs number, and M is three-dimensional point cloud number of coordinates；
5) by unit N in adjacent Unit twoiThe camera parameter R of middle common sequence photoh、TgAs its adjacent next unit Ni+1's
The initial value of the camera parameter of common sequence photo, and solve its adjacent next unit Ni+1In other two sequence photographs phase
Machine parameter Rh、TgWith three-dimensional point cloud coordinate Mj, unit N is calculated with thisi+1Every sequence photographs in other two sequence photographs
Camera parameter Rh、TgWith three-dimensional point cloud coordinate Mj；
6) by the camera parameter R of all sequence photographsh、TgWith three-dimensional point cloud coordinate MjAs the input of PMVS algorithm, thus
To the seed dough sheet of scenario objects；
7) extension of seed dough sheet: by sequence photographs IkIt is divided into several image blocks Co(x, y) has using neighboring seeds dough sheet
There are position and the similar characteristic of normal direction to be extended seed dough sheet, to make the gradually coverage goal object of the seed dough sheet after extension
The surface of body；
8) seed dough sheet filters: rejecting extended error using local gray level consistency constraint and globally visible consistency constraint
Seed dough sheet.
2. a kind of three-dimensional rebuilding method of large scene object according to claim 1, it is characterised in that: camera in step 1)
When around scene objects acquisition sequence photo, the angle between adjacent two sequence photographs is 5 °~15 °, the sequence of acquisition
Number of pictures is odd number, and the sequence photographs of acquisition are at least 3.
3. a kind of three-dimensional rebuilding method of large scene object according to claim 1, it is characterised in that: pass through in step 7)
The extension of seed dough sheet in each image block so that at least reconstruct a seed dough sheet.
4. a kind of three-dimensional rebuilding method of large scene object according to claim 1, it is characterised in that: extension in step 8)
The seed dough sheet of mistake includes external seed dough sheet and internal seeds dough sheet, and the extension of seed dough sheet, filtering are circuited sequentially iteration 3
It is secondary that target object surface is completely covered by dense seed dough sheet.
5. a kind of three-dimensional rebuilding method of large scene object according to claim 2, it is characterised in that: the sequence of acquisition is shone
Piece is 45.
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