CN103236043B - A kind of plant organ point cloud restoration method - Google Patents
A kind of plant organ point cloud restoration method Download PDFInfo
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- CN103236043B CN103236043B CN201310154400.3A CN201310154400A CN103236043B CN 103236043 B CN103236043 B CN 103236043B CN 201310154400 A CN201310154400 A CN 201310154400A CN 103236043 B CN103236043 B CN 103236043B
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Abstract
The invention provides a kind of plant organ point cloud restoration method, relate to the reparation field of 3-D view.Comprise following steps: S1, structure plant organ point cloud template, generate some cloud template set; S2, disappearance organ point cloud to be mated with described some cloud template set, find out the some cloud template the most close with described disappearance organ point cloud; S3, based on described some cloud template the most close, missing point cloud to be repaired.The present invention, by solving the reparation problem of bulk plant organ point cloud shortage of data, can obtain complete point cloud data in plant scanning process.It is high that the present invention repairs accuracy, repair some cloud and original point cloud amalgamation good, reparation part can reflect the principal character of plant organ.
Description
Technical field
The present invention relates to the reparation field of 3-D view, be specifically related to a kind of plant organ point cloud restoration method.
Background technology
In recent years, along with reaching its maturity of three-dimensional laser scanning technique and extensively popularizing of related equipment, utilize laser scanning data to carry out three-dimensional reconstruction and become study hotspot, in phytomorph structure measurement and analysis, it is slow to there is speed in the collecting method of traditional-handwork, the shortcomings such as precision is low, be difficult to meet scientific research requirement, and Measurement Technology of 3 D Laser Scanning can overcome the limitation that traditional-handwork is measured to a great extent, noncontact Active measuring mode is adopted directly to obtain high-precision three-dimensional data, can scan arbitrary objects, and it is fast to have sweep velocity, real-time, precision high.But phytomorph structure is comparatively complicated, carry out in the three dimensional point cloud acquisition process of plant organ utilizing spatial digitizer, blocking (as maize leaf is wrapped in maize ear) between the wax attribute (as pepper fruit, Apple) on Chang Yinwei plant organ surface or organ, scanning result is subject to the interference of luminous environment, therefore often there is more missing data in direct scanning in the three dimensional point cloud obtained from plant, and institute's lack part mostly is bulk point cloud disappearance, lacks different from the small holes of other field.These lack part not only make to be difficult to build plant organ geometric model, model to be carried out to the calculating of the geometry such as area, volume, also can affect the follow-up process etc. of model.Therefore, plant organ point cloud reparation is an important process of the Plant Morphologic based on laser three-dimensional scanning data.
In the hole repair of three-dimensional point cloud, carried out a few thing both at home and abroad, the domestic patent found of typical work has following several sections:
First section is " method for filling dot cloud hole based on the 3-D scanning of B-spline surface ", publication number is CN 1945626A, a kind of method for filling dot cloud hole that can provide the 3-D scanning based on B-spline surface of surface fitting degree of accuracy of this disclosure of the invention.
Second section is " based on the complementing method of the monumented point hole of neural network in 3-D scanning point cloud ", publication number is CN 101127123A, this invention provide a kind of can ensure to fill up hole data and ambient data continuously and put cloud character representation better based on the method for filling dot cloud hole of the 3-D scanning of neural network, this invention has the simple advantage of method.
3rd section is " method for filling dot cloud hole of 3-D scanning ", publication number is CN 1858801A, a kind of method for filling dot cloud hole of the 3-D scanning based on triangular domain bezier surface of disclosure of the invention, it comprises mock-up, 3-D data collection, data prediction, curve and surface reconstruct are processed with analysis, surface fitting, cad model modeling, data programing, part, measured database, CAD simulated reservoir link.This invention adopts the method for surface fitting can obtain the curved surface of scattered point set around an Accurate Curve-fitting hole, thus can ensure higher fitting precision when getting a some perforations adding on curved surface.
4th section " pretreatment method used in three-dimensional processing of thin-walled complicated ", publication number is CN102081693A, utilizes completion method in kind by sweep trace repair mode one by one, realizes the rectangle topology reparation to whole thin-wall complicated curved surface cloud data; Reparation pre-service is carried out to the hole in the sweep trace point cloud raw data of thin-wall complicated curved surface part or skewness region.
In above-mentioned prior art, method mostly be for a fritter dot cloud hole or targetedly industrial part carried out, research for plant organ point cloud shortage of data is less, and substantially all cannot solve the reparation problem of bulk plant organ point cloud shortage of data, namely these methods are mostly for specific absent region, carrying out, in reparation, there is no more perfect work for plant organ point cloud disappearance.
Summary of the invention
(1) technical matters solved
For the deficiencies in the prior art, the invention provides a kind of plant organ point cloud restoration method.Conventional some cloud is repaired and is mostly to utilize the geometric properties of to be repaired some cloud itself to repair, it is of the present invention that to consider plant sample more, there is between homolog higher similarity, again the reparation realizing missing point cloud is contrasted to missing point cloud and template based on first building template, solve the reparation problem of bulk plant organ point cloud shortage of data, make to obtain complete point cloud data in plant scanning process.
(2) technical scheme
For realizing above object, the present invention is achieved by the following technical programs:
A kind of plant organ point cloud restoration method, comprises following steps:
S1, structure plant organ point cloud template, generate some cloud template set;
S2, disappearance organ point cloud to be mated with described some cloud template set, find out the some cloud template the most close with described disappearance organ point cloud;
S3, based on described some cloud template the most close, missing point cloud to be repaired.
Wherein, step S1 comprises following steps:
S11, utilize the plant organ A of spatial digitizer to pre-structured template to carry out 3-D scanning, the initial three dimensional point cloud of plant organ A is carried out a cloud segmentation, denoising and resampling process, obtain the some cloud template E of plant organ
i;
The method of S12, employing step S11 builds N number of form plant organ point cloud template similar to disappearance organ, generates a some cloud template set V
a={ E
i, i=1 ..., N}, and the point patterns histogram PFH information calculating each point cloud is as a feature for cloud template.
Wherein, the point patterns histogram PFH computing method of step S12 point cloud are: use Harris key point detection subalgorithm to obtain key point to each point cloud, then by Quick-Point feature histogram FPFH feature assessment method to a cloud computing feature.
Wherein, step S2 comprises following steps:
S21, disappearance organ point cloud is designated as A
#, calculate A
#point patterns histogram PFH feature;
S22, at V
ain found out and A by feature matching method
#feature is points of proximity cloud template E the most
k, calculate described A respectively
#with described E
kbounding box, by described E
kbounding box according to A
#the length, width and height ratio of bounding box carries out equal proportion convergent-divergent, E
ktemplate after convergent-divergent is designated as
Wherein, step S3 comprises following steps:
S31, by described A
#generate corresponding triangle gridding, be designated as M
a; Described in inciting somebody to action
generate corresponding triangle gridding, be designated as M
e;
S32, employing grid deforming method are by described M
ebe deformed into
wherein
for M
aregion corresponding after distortion,
for M
aabsent region;
Described in S33, general
with M
acarry out Mesh smoothing fusion, after fusion, namely complete the some cloud reparation of disappearance organ.
Wherein, have employed the constraint making deformation energy minimum in the deformation process of the grid deforming method adopted in step S32.
Wherein, the guarantor's feature triangular mesh smoothing algorithm based on weighted least-squares is adopted to carry out Mesh smoothing fusion in step S33.
(3) beneficial effect
The present invention, by providing a kind of plant organ point cloud restoration method, by solving the reparation problem of bulk plant organ point cloud shortage of data, can obtain complete point cloud data in plant scanning process.This method repair accuracy high, repair some cloud and original point cloud amalgamation good, reparation part can reflect the principal character of plant organ.
Accompanying drawing explanation
Fig. 1, plant organ point cloud restoration method process flow diagram;
The stem stalk three-dimensional point cloud atlas of Fig. 2, disappearance;
The stem stalk three-dimensional point cloud atlas of Fig. 3, reparation;
The schematic diagram of Fig. 4, calculation level feature histogram PFH.
Embodiment
Under regard to a kind of plant organ point cloud restoration method proposed by the invention, describe in detail in conjunction with the accompanying drawings and embodiments.
Embodiment 1:
First set up various plants organ point cloud template, mainly repair for the some cloud disappearance problem produced in whole plant 3-D scanning process.
Organ template: popular organ template is exactly first to build up the complete organ point cloud of some forms, and they are as template, and the organ point cloud of disappearance and its contrast supplement the part lacked in organ; Same organs template set is topological isomorphic with disappearance organ.
The flow process of the present embodiment is: remember that the A organ point cloud having to lack is A
#, the organ (N number of sample) that we select and its form is approximate carries out destructiveness and samples, and sets up complete organ template respectively, forms organ template set V
a={ E
i, i=1 ..., N}, wherein E
ibe i-th template.The work that we will do is exactly from V
amiddle selection one is the immediate template E of form with it
k, and by A
#the part of disappearance is repaired by the method for coupling.
Specific implementation process comprises following steps:
S1, structure plant organ point cloud template, generate some cloud template set;
Step S1 comprises following steps:
S11, utilize the plant organ A of spatial digitizer to pre-structured template to carry out 3-D scanning, the initial three dimensional point cloud of plant organ A is carried out a cloud segmentation, denoising and resampling process, obtain the some cloud template E of plant organ
i, E
ifor complete crop organ's three-dimensional point cloud geometric model.
The method of S12, employing step S11 builds N number of form plant organ point cloud template similar to disappearance organ for disappearance organ, generates a some cloud template set V
a={ E
i, i=1 ..., N}, and point patterns histogram PFH (the Point Feature Histograms) information calculating wherein each point cloud is as a feature for cloud template.
The point patterns histogram PFH computing method of step S12 point cloud are: use Harris key point detection subalgorithm to obtain key point to each point cloud, then by Quick-Point feature histogram FPFH (Fast Point Feature Histograms) feature assessment method to a cloud computing feature.
The point patterns histogram concrete grammar calculating set point cloud is as follows:
First the point in the arbitrfary point K neighborhood in reply point cloud is to determining source data point, then the some calculation level feature composition characteristic vector to source data point K neighborhood, finally all result statistics is obtained exporting histogram.Concrete steps are as follows:
(1) to P
iarbitrfary point in some K-neighborhood is to determining source data point;
First, Arbitrary 3 D point P in a cloud is asked for
ithe average curvature of curved surface that place produces.Namely to P
isearch is at P
ithe normal vector (K-neighborhood) of the neighborhood point in the r radius ball of point.After all normal vectors of acquisition, the Viewing-angle information existed is utilized to reorientate them.If
so n
pi=-n
pi.Wherein, n
pip
inormal vector, v is visual angle, || v-p
i|| be v-p
imould.
To the arbitrfary point in P point K neighborhood to p
i, p
jand estimation technique vector n (j<i)
i, n
j, select a source data point P
sand a number of targets strong point P
t.Here P
spoint is estimate vector and P
t-P
sthe point that vector angle is less, even <n
i, p
j-p
i>≤<n
j, p
i-p
j>, then P
s=P
i, P
t=P
j.Otherwise, P
s=P
j, P
t=P
i.
(2) four kinds of features are calculated, to the some cloud computing point patterns histogram PFH of k value neighborhood;
Be defined as follows in source data point: u=n
s, v=(P
t-P
s) × u, w=u × v, is illustrated in figure 4 the schematic diagram of calculation level feature histogram PFH;
Four features are designated as f respectively
1, f
2, f
3, f
4, calculate by such as giving a definition:
f
1＝<v,n
1>
f
2＝||P
t-P
s||
f
3＝<u,P
t-P
s>/f
2
f
4＝atan(<w,n
t>,<u,n
t>)
Wherein, four kinds of features are that angle between the normal vector of point-to-point transmission point and distance vector is weighed.F
1and f
3the cosine value of tri-vector angle, f
4n
twith the angle that u is formed, f
2for P
twith P
sbetween distance value.
Four features are divided into multiple interval to be compared with the power of each feature of diacritical point centering, to feature f
ithe set-point s of setting one in its span
ibe divided into two intervals, with step (s
i, f
i) power of representation feature carries out evaluation.If f
i<s
i, make step (s
i, f
i) etc. 0.Otherwise, be 1.Try to achieve idx value by formula below again, all result statistics are obtained exporting histogram.Wherein, to the point in an all K neighborhood of cloud P point to calculating, idx is in order to mark every stack features vector.As in histogram 7, horizontal ordinate represents the number percent comprising in neighborhood and have and always count shared by the source point of this feature for the ordinate value of its correspondence of idx value.
In order to improve travelling speed, the PFH account form simplified is adopted to calculate the geometric properties of cloud data, i.e. Quick-Point feature histogram (FPFH).First calculate 3 eigenwerts between query point and its neighborhood except distance feature, form the point patterns histogram (SPFH) simplified.Then redefine the K neighborhood of each point, use contiguous SPFH value to calculate the final FPFH of query point.FPFH has synthesized histogram by decomposition three eigenwerts simplification, is slit into 11 intervals, then draws separately each characteristic dimension each eigenwert average mark, and finally link together statistics generation feature histogram.
S2, disappearance organ point cloud to be mated with described some cloud template set, find out the some cloud template the most close with described disappearance organ point cloud;
Step S2 comprises following steps:
S21, disappearance organ point cloud is designated as A
#, calculate A
#pFH feature;
S22, at V
ain found out and A by feature matching method
#feature is points of proximity cloud template E the most
k(concrete grammar is by A
#point patterns histogram and the point patterns histogram of all templates contrast, and find out most similar one group), calculate A respectively
#with E
kbounding box, by template E
kbounding box is according to A
#the length, width and height ratio of bounding box carries out equal proportion convergent-divergent, E
ktemplate after convergent-divergent is designated as
S3, based on described some cloud template the most close, missing point cloud to be repaired;
Step S3 comprises following steps:
S31, by described A
#generate corresponding triangle gridding, be designated as M
a; Described in inciting somebody to action
generate corresponding triangle gridding, be designated as M
e;
Delaunay triangulation methodology is adopted by the method for a cloud generating mesh.
S32, employing grid deforming method are by described M
ebe deformed into
wherein
for M
aregion corresponding after distortion,
for M
alack part;
Described in S33, general
with M
acarry out Mesh smoothing fusion, after fusion, namely complete the some cloud reparation of disappearance organ;
Wherein, have employed the constraint making deformation energy (as characteristic energy, rigid energy etc.) minimum in the deformation process of the grid deforming method adopted in step S32.
In employing based in guarantor's feature grid deformation method process of differential coordinate energy function:
The energy function of distortion of the mesh is the constraint condition as control mesh local geometric features, and the energy function form based on differential coordinate is:
Wherein T
ifor partial transformation, N (i)={ j|{i, j} ∈ E} is vertex v
iface and connect vertex set, w
ijrepresent vertex v
iand v
jbetween power, meet
it calculates and adopts w
ij=1/N (i).By minimizing deformation energy function, calculate the new apex coordinate that the apex coordinate obtained is the rear grid of distortion, its mathematic(al) representation is:
V＝argmin
VE(V)
This formula can be converted into matrix form, and adopt Cholesky decomposition method solve this problem namely obtain be out of shape after grid.
Wherein, the guarantor's feature triangular mesh smoothing algorithm based on weighted least-squares is adopted to carry out Mesh smoothing fusion in step S33.
Embodiment 2:
For corn fruit; corn fruit some wrapped up by stem stalk and blade; obtain complete some cloud if normal and must carry out destructiveness sampling; but so just damage the one-piece construction of milpa; so we can utilize the fruit of other plant (not carrying out whole plant scanning) to build template; then complete the some cloud reparation pre-modeling plant lacking corn fruit, be illustrated in figure 2 the stem stalk three-dimensional point cloud atlas of corn disappearance, Fig. 3 repairs the model well.
Above embodiment is only for illustration of the present invention; and be not limitation of the present invention; the those of ordinary skill of relevant technical field; without departing from the spirit and scope of the present invention; can also make a variety of changes and modification; therefore all equivalent technical schemes also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.
Claims (5)
1. a plant organ point cloud restoration method, is characterized in that, comprises following steps:
S1, structure plant organ point cloud template, generate some cloud template set;
S2, disappearance organ point cloud to be mated with described some cloud template set, find out the some cloud template the most close with described disappearance organ point cloud;
S3, based on described some cloud template the most close, missing point cloud to be repaired;
Step S1 comprises following steps:
S11, utilize the plant organ A of spatial digitizer to pre-structured template to carry out 3-D scanning, the initial three dimensional point cloud of plant organ A is carried out a cloud segmentation, denoising and resampling process, obtain the some cloud template E of plant organ
i;
The method of S12, employing step S11 builds N number of form plant organ point cloud template similar to disappearance organ, generates a some cloud template set V
a={ E
i, i=1 ..., N}, and the point patterns histogram PFH information calculating each point cloud is as a feature for cloud template;
Step S2 comprises following steps:
S21, disappearance organ point cloud is designated as A
#, calculate A
#point patterns histogram PFH feature;
S22, at V
ain found out and A by feature matching method
#feature is points of proximity cloud template E the most
k, calculate described A respectively
#with described E
kbounding box, by described E
kbounding box according to A
#the length, width and height ratio of bounding box carries out equal proportion convergent-divergent, E
ktemplate after convergent-divergent is designated as
2. a kind of plant organ point cloud restoration method as claimed in claim 1, it is characterized in that, the point patterns histogram PFH computing method of step S12 point cloud are: use Harris key point detection subalgorithm to obtain key point to each point cloud, then by Quick-Point feature histogram FPFH feature assessment method to a cloud computing feature.
3. a kind of plant organ point cloud restoration method as claimed in claim 1, it is characterized in that, step S3 comprises following steps:
S31, by described A
#generate corresponding triangle gridding, be designated as M
a; Described in inciting somebody to action
generate corresponding triangle gridding, be designated as M
e;
S32, employing grid deforming method are by described M
ebe deformed into
wherein
for M
aregion corresponding after distortion,
for M
aabsent region;
Described in S33, general
with M
acarry out Mesh smoothing fusion, after fusion, namely complete the some cloud reparation of disappearance organ.
4. a kind of plant organ point cloud restoration method as claimed in claim 3, is characterized in that, have employed the constraint making deformation energy minimum in the deformation process of the grid deforming method adopted in step S32.
5. a kind of plant organ point cloud restoration method as claimed in claim 3, is characterized in that, adopts the guarantor's feature triangular mesh smoothing algorithm based on weighted least-squares to carry out Mesh smoothing fusion in step S33.
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| CN107784138B (en) * | 2016-08-26 | 2021-03-26 | 上海宝冶集团有限公司 | Point cloud gravity deformation correction method based on structural mechanics analysis |
| CN106845416B (en) * | 2017-01-20 | 2021-09-21 | 百度在线网络技术（北京）有限公司 | Obstacle identification method and device, computer equipment and readable medium |
| CN107369211B (en) * | 2017-07-13 | 2020-09-25 | 云南数云信息科技有限公司 | Ancient building three-dimensional point cloud acquisition system and model self-correction method |
| CN107343148B (en) * | 2017-07-31 | 2019-06-21 | Oppo广东移动通信有限公司 | Image completion method, apparatus and terminal |
| CN108198145B (en) * | 2017-12-29 | 2020-08-28 | 百度在线网络技术（北京）有限公司 | Method and device for point cloud data restoration |
| CN111161352B (en) * | 2019-12-30 | 2023-11-03 | 熵智科技(深圳)有限公司 | Object identification method and device based on triangular mesh simplified template |
| CN111444811B (en) * | 2020-03-23 | 2023-04-28 | 复旦大学 | Three-dimensional point cloud target detection method |
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