CN104318612A - 3D space point selecting method - Google Patents
3D space point selecting method Download PDFInfo
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- CN104318612A CN104318612A CN201410583018.9A CN201410583018A CN104318612A CN 104318612 A CN104318612 A CN 104318612A CN 201410583018 A CN201410583018 A CN 201410583018A CN 104318612 A CN104318612 A CN 104318612A
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- 238000000034 method Methods 0.000 title claims abstract description 58
- 238000009792 diffusion process Methods 0.000 claims abstract description 7
- 238000005070 sampling Methods 0.000 claims description 12
- 230000001174 ascending effect Effects 0.000 claims description 3
- 238000012800 visualization Methods 0.000 abstract 1
- 230000000007 visual effect Effects 0.000 description 4
- 208000024780 Urticaria Diseases 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 2
- 230000037237 body shape Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/10—Constructive solid geometry [CSG] using solid primitives, e.g. cylinders, cubes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/04—Indexing scheme for image data processing or generation, in general involving 3D image data
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- Computer Graphics (AREA)
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- General Physics & Mathematics (AREA)
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- Processing Or Creating Images (AREA)
Abstract
The invention provides a 3D space point selecting method. The method comprises the steps that in a 2D plane, a front-end visible plane in the 2D range is defined with a location point selected by a user as the center, diffusion is conducted along the Z axis to form a 3D perspective column, candidate points on the 3D perspective column are grouped, an optimal group meeting the intention of the user is selected, the average value of the optimal group is selected as a Z value of a 3D spatial point, and finally the 3D spatial point selected by the user is determined. According to the 3D space point selecting method, point selecting is conducted in the visible range according to the point selecting intention of the user, visualization of point selecting is realized; moreover, the method has the advantages that users in different industries can conduct point selecting according to the needs of the industries or the needs of themselves, interference points in point groups are eliminated, the point selecting accuracy is further improved, meanwhile, more time is saved and more convenience is brought to the users in the 3D point selecting process, automatic and intelligent point selecting is realized, and therefore the working efficiency of the users is greatly improved.
Description
Technical field
The invention belongs to 3d space modeling technique field, particularly relate to a kind of 3d space point sampling method in 3d space modeling process.
Background technology
At present; the point choosing space ad-hoc location is often needed in the middle of 3D cartoon technique, production of film and TV and 3D Model Reconstruction, but, the situation of interference mutually usually can be there is when spatial point number is more; make the operating process of pickup point compare very complicated, mainly contain some reason following:
(1) there is inherent ambiguousness in 2D pickup point: the various display devices used at present, such as plasma panel, liquid crystal display, rear-projection TV etc. are all plane display, both display frame was 2D image, therefore, each point has the overlap of each layer scenery on different depth, when carry out picking up wherein certain is some time, point overlapping in the degree of depth is too many, and user cannot choose the point wherein wanted accurately;
(2) in the process due to 2D pickup point, point overlapping in its degree of depth is too many, and the point of different depth will cause interference to the sight line of user, and user is difficult to remove interference;
(3) user is carrying out in reconnaissance process, the sight line adult shape that eyes are launched, and can there is certain interval between selected point and other points, belong to blank space, and when carrying out reconnaissance inside body shape, when being positioned at blank space, cannot be difficult to choose accurately.
Therefore, how conveniently to choose the point of space ad-hoc location in the 3 d space, reach the object of accurate modeling, and make the user of different industries can need to carry out reconnaissance according to industry or self, become problem demanding prompt solution.
Summary of the invention
The present invention is directed to above-mentioned technical matters, propose a kind of 3d space point sampling method, the method can make the user of different industries can need to carry out reconnaissance according to industry or self, not only increase the accuracy of reconnaissance, and make user more efficient and convenient in reconnaissance process, substantially increase work efficiency.
Technical scheme of the present invention is:
Compared to the prior art, the invention provides a kind of 3d space point sampling method, described method comprises:
Steps A 1: in 2D plane, user chooses a location point S
0, record clicking point planimetric coordinates (X
0, Y
0), wherein, X
0refer to the numerical value of the user present position on horizontal X axle, Y
0refer to the numerical value of user present position in vertical direction;
Steps A 2: with the location point S chosen
0centered by draw a circle to approve the front end visible planar of 2D scope;
Steps A 3: the front end visible planar of 2D scope is spread along Z-direction according to perspective relation, form 3D and have an X-rayed post, and the point removed outside perspective post, for the ease of observing perspective post, post will be had an X-rayed visual, the 3D observed 3D perspective post delineation user has an X-rayed post scope, and the Z value that among the visible range on Z axis, distance users is nearest is designated as Z
nearest, distance users Z value is farthest designated as Z
farest;
Steps A 4: the candidate point that 3D has an X-rayed on post is hived off, the method dividing group can adopt but be not limited to adjacent spacing threshold criterion or K-means grouping method;
Steps A 5: judge to meet the optimum group that user chooses intention;
Steps A 6: the member choosing optimum group puts average depth value Z
meanas the Z value of 3d space selected point;
Steps A 7: finally determine that 3d space point S, the S coordinate record that user chooses is (X
0, Y
0, Z
mean).
Based on such scheme, the present invention also improves as follows:
Described grouping method adopts adjacent spacing threshold decision method or K-means grouping method;
Described adjacent spacing threshold decision method comprises the following steps:
Step C1: the threshold value setting a depth interval distance, as the case may be definite threshold size;
Step C2: the threshold value according to setting adopts ascending order or descending sort to candidate point according to its degree of depth Z value;
Step C3: judge consecutive point spacing, if consecutive point Z spacing is greater than threshold value, is then judged to be distinct group, if consecutive point Z spacing is less than threshold value, is then judged to be same a group.
Described K-means grouping method is a kind of passing method material point cluster of a large amount of higher-dimension hived off, and a central point or representative point can be selected in its each subgroup be divided into; The present invention's application K-means method, hives off for the one-dimensional vector Z value or tri-vector XYZ value of having an X-rayed all candidate points in post; Wherein X, Y, Z value is respectively three side-play amounts axially in three dimensions, wherein, and horizontal direction-X-axis, vertical direction-Y-axis, depth direction-Z axis.
The decision condition that described user chooses intention is at least from observer front point group more nearby, from the one in the point group close to perspective post central shaft or the larger point group of affiliated some population density; Wherein, the point group more nearby from observer front, namely degree of depth Z value presses close to the group of observer most, according to
to obtain in point group the mean value of the distance a little and between observer, wherein
for arriving a little the mean value of observer's distance inside selected point group, Z
ifor the Z value of any point in point group on Z axis, Z
eyefor the Z value of observer position, obtain the Z value mean value of all point groups, choosing wherein reckling is the point group met.
If described user chooses the decision condition of intention for more than one, then employing linear weighted function synthetic determination or nonlinear weight are the synthetic determination method of representative; Wherein, linear weighted function synthetic determination, supposes that place's its coordinate of any point i is in space (X
i, Y
i, Z
i), with user-selected point (X
0, Y
0, Z
0) mean value of distance, can be expressed as
being weighted three asks its mean value to divide, and chooses optimum group, wherein X
i, Y
i, Z
ibe respectively the X value of space any point, Y value, Z value, X
eye, Y
eye, Z
eyebe respectively the X value of observer position, Y value, Z value.
The shape of described diffusion is circular or square, and diffusion shape does not limit, and user can follow principle simply and easily and select voluntarily.
Technique effect of the present invention is:
The present invention is directed to the intention of user's selected point, provide a kind of 3d space and get method a little, the method is carried out diffuseing to form 3D by delineation 2D plane along Z axis and is had an X-rayed post, and get a little in visual range, achieve the visual of reconnaissance, and the method can make the user of different industries can need to carry out reconnaissance according to industry or self, get rid of the noise spot among point group, further increase the accuracy of reconnaissance, also make user carry out more saving time in the process that 3D gets a little simultaneously, convenient, achieve the intellectually and automatically of reconnaissance, thus substantially increase the work efficiency of user.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of 3d space point sampling method of the present invention.
Fig. 2 is that the present invention forms according to Z-direction the schematic diagram that 3D has an X-rayed post.
Fig. 3 is the schematic diagram that the present invention hives off according to Z-direction.
Embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described:
Embodiment:
Shown in Fig. 1-3, the invention provides a kind of 3d space point sampling method, described method comprises:
Steps A 1: in 2D plane, user chooses a location point S
0, record clicking point planimetric coordinates (X
0, Y
0), wherein, X
0refer to the numerical value of the user present position on horizontal X axle, Y
0refer to the numerical value of user present position in vertical direction;
Steps A 2: with the location point S chosen
0centered by draw a circle to approve the front end visible planar of 2D scope;
Steps A 3: the front end visible planar of 2D scope is spread along Z-direction according to perspective relation, form 3D and have an X-rayed post, and the point removed outside perspective post, the shape of described diffusion is circular or square, and diffusion shape does not limit, and user can follow principle simply and easily and select diffusion shape voluntarily, for the ease of observing perspective post, to have an X-rayed post visual, the 3D observed 3D perspective post delineation user has an X-rayed post scope, and the Z value that among the visible range on Z axis, distance users is nearest is designated as Z
nearest, distance users Z value is farthest designated as Z
farest;
Steps A 4: hive off to the candidate point that 3D has an X-rayed on post, described grouping method adopts adjacent spacing threshold decision method or K-means grouping method;
Described adjacent spacing threshold decision method comprises the following steps:
Set the threshold value of a depth interval distance, as the case may be definite threshold size;
Threshold value according to setting adopts ascending order or descending sort to candidate point according to its degree of depth Z value;
Consecutive point spacing is judged, if consecutive point Z spacing is greater than threshold value, is then judged to be distinct group, if consecutive point Z spacing is less than threshold value, be then judged to be same a group;
Described K-means grouping method is a kind of passing method material point cluster of a large amount of higher-dimension hived off, and a central point or representative point can be selected in its each subgroup be divided into; The present invention's application K-means method, hives off for the one-dimensional vector Z value or tri-vector XYZ value of having an X-rayed all candidate points in post; Wherein X, Y, Z value is respectively three side-play amounts axially in three dimensions, wherein, and horizontal direction-X-axis, vertical direction-Y-axis, depth direction-Z axis;
Steps A 5: judge to meet the optimum group that user chooses intention;
The decision condition that described user chooses intention is at least from observer front point group more nearby, from the one in the point group close to perspective post central shaft or the larger point group of affiliated some population density; Wherein, the point group more nearby from observer front, namely degree of depth Z value presses close to the group of observer most, according to
to obtain in point group the mean value of the distance a little and between observer, wherein
for arriving a little the mean value of observer's distance inside selected point group, Z
ifor the Z value of any point in point group on Z axis, Z
eyefor the Z value of observer position, obtain the Z value mean value of all point groups, choosing wherein reckling is the point group met.
If described user chooses the decision condition of intention for more than one, then employing linear weighted function synthetic determination or nonlinear weight are the synthetic determination method of representative; Wherein, linear weighted function synthetic determination, supposes that place's its coordinate of any point i is in space (X
i, Y
i, Z
i), with user-selected point (X
0, Y
0, Z
0) mean value of distance, can be expressed as
being weighted three asks its mean value to divide, and chooses optimum group, wherein X
i, Y
i, Z
ibe respectively the X value of space any point, Y value, Z value, X
eye, Y
eye, Z
eyebe respectively the X value of observer position, Y value, Z value;
Steps A 6: the member choosing optimum group puts average depth value Z
meanas the Z value of 3d space selected point;
Steps A 7: finally determine that 3d space point S, the S coordinate record that user chooses is (X
0, Y
0, Z
mean).
Claims (6)
1. a 3d space point sampling method, is characterized in that, described method comprises:
Steps A 1: in 2D plane, user chooses a location point S
0, record clicking point planimetric coordinates (X
0, Y
0);
Steps A 2: with the location point S chosen
0centered by draw a circle to approve the front end visible planar of 2D scope;
Steps A 3: the front end visible planar of 2D scope spread along Z-direction according to perspective relation, forms 3D and has an X-rayed post, and removes the point outside perspective post;
Steps A 4: the candidate point that 3D has an X-rayed on post is hived off;
Steps A 5: judge to meet the optimum group that user chooses intention;
Steps A 6: the member choosing optimum group puts average depth value Z
meanas the Z value of 3d space selected point;
Steps A 7: finally determine that 3d space point S, the S coordinate record that user chooses is (X
0, Y
0, Z
mean).
2. 3d space point sampling method as claimed in claim 1, is characterized in that: described grouping method adopts adjacent spacing threshold decision method or K-means grouping method.
3. 3d space point sampling method as claimed in claim 2, is characterized in that: described adjacent spacing threshold decision method comprises the following steps:
Step C1: the threshold value setting a depth interval distance, as the case may be definite threshold size;
Step C2: the threshold value according to setting adopts ascending order or descending sort to candidate point according to its degree of depth Z value;
Step C3: judge consecutive point spacing, if consecutive point Z spacing is greater than threshold value, is then judged to be distinct group, if consecutive point Z spacing is less than threshold value, is then judged to be same a group.
4. 3d space point sampling method as claimed in claim 1, is characterized in that: the decision condition that described user chooses intention is at least from observer front point group more nearby, from the one in the point group close to perspective post central shaft and the larger point group of affiliated some population density.
5. 3d space point sampling method as claimed in claim 4, is characterized in that: if described user chooses the decision condition of intention for more than one, then employing linear weighted function synthetic determination or nonlinear weight are the synthetic determination method of representative.
6. 3d space point sampling method as claimed in claim 1, is characterized in that: the shape of described diffusion is for circular or square.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410583018.9A CN104318612B (en) | 2014-10-27 | 2014-10-27 | A kind of 3d space point sampling method |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410583018.9A CN104318612B (en) | 2014-10-27 | 2014-10-27 | A kind of 3d space point sampling method |
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| CN104318612B CN104318612B (en) | 2018-04-13 |
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|---|---|---|---|
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103310489A (en) * | 2013-06-24 | 2013-09-18 | 中南大学 | Three-dimensional model interactive method based on dynamitic depth hierarchy structure |
| CN103489224A (en) * | 2013-10-12 | 2014-01-01 | 厦门大学 | Interactive three-dimensional point cloud color editing method |
| CN103870845A (en) * | 2014-04-08 | 2014-06-18 | 重庆理工大学 | Novel K value optimization method in point cloud clustering denoising process |
-
2014
- 2014-10-27 CN CN201410583018.9A patent/CN104318612B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103310489A (en) * | 2013-06-24 | 2013-09-18 | 中南大学 | Three-dimensional model interactive method based on dynamitic depth hierarchy structure |
| CN103489224A (en) * | 2013-10-12 | 2014-01-01 | 厦门大学 | Interactive three-dimensional point cloud color editing method |
| CN103870845A (en) * | 2014-04-08 | 2014-06-18 | 重庆理工大学 | Novel K value optimization method in point cloud clustering denoising process |
Non-Patent Citations (2)
| Title |
|---|
| 孙红岩 等: "基于K-means聚类方法的三维点云模型分割", 《计算机工程与应用》 * |
| 雷敏 等: "一种三维点云聚类算法的研究", 《科学工程与技术》 * |
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