CN108010136A - A kind of DISCRETE ANALYSIS METHOD towards cluster three-dimensional house property - Google Patents

Abstract

The invention discloses a discrete analysis method for clustered three-dimensional real estate. Firstly, the clustered three-dimensional real estate is generated according to the plane structure diagram of the real estate, the floor height and the number of floors; The adjacency relationship with the surrounding real estate units; then select one or more interested real estate units, calculate the translation vector and zoom size of other real estate units according to the deformation function, so that the interested real estate units are highlighted; finally, according to the recorded adjacency relationship, The center points connecting the "adjacent" faces express the actual spatial relationship. The present invention can clearly display the internal real estate units that originally have visual barriers, is efficient and effective, and is based on the real estate plane structure diagram, meeting the visualization requirements in practical applications.

Description

A Discrete Analysis Method for Cluster-Oriented Three-Dimensional Real Estate

technical field

The invention belongs to the technical field of three-dimensional object visualization, and relates to a visualization method for clustered three-dimensional real estate, in particular to a discrete analysis method for clustered three-dimensional real estate, which can display the details of real estate units that block the line of sight in the three-dimensional scene through deformation transformation information while maintaining the overall topological relationship of the cluster properties.

Background technique

In the real world, many solid objects have dense cluster characteristics, and cluster objects often cause objective visual obstruction, making it difficult to observe solid objects in the cluster. At present, the visualization methods of cluster objects include viewpoint roaming, cross-section cutting, target object separation and transparency and so on. Viewpoint roaming is a common function and implementation in 3D application software, which allows observation of views in all directions, but lacks background information and reference objects, and has a weak sense of direction inside cluster objects, and there is always the problem of line of sight blocking; cross-section cutting Internal features can be observed, but there is only information on one cross-section, and the interaction is cumbersome; target object separation is to artificially extract the target object from the cluster object for observation. This method is complicated to operate and loses the target The background reference information of the object; the transparency can realize the observation of the internal object in the visualization effect, but there is still a block in the line of sight from the observer's viewpoint to the target object. None of the above methods can well balance the visualization requirements between clusters and individuals. While highlighting individual objects, they destroy the properties of cluster objects such as shape and spatial distribution.

There are many dense 3D entities such as real estate units, office buildings and shops in the city. Now many 3D software can provide high-level simulation of urban buildings, and can even be extended to 3D rendering of real estate units. However, it is not easy to observe the internal conditions in clustered real estate. Real estate units are prone to obstructions in sight. How to fully display the real estate units in cluster real estate while maintaining the overall characteristics of the cluster has become a key issue in the visualization of cluster 3D objects.

Contents of the invention

In order to solve the above problems, the present invention proposes a cluster-oriented three-dimensional real estate discrete analysis method, which simultaneously highlights the real estate units that users are interested in in the cluster real estate, weakens the information display of other real estate units, and maintains the spatial relationship of the cluster objects as much as possible.

The technical scheme adopted in the present invention is: a kind of discrete analysis method facing cluster three-dimensional real estate, it is characterized in that, comprises the following steps:

Step 1: Generate clustered three-dimensional real estate according to the floor plan, floor height and number of floors of the real estate;

Step 2: uniquely identify the information of each real estate unit in the three-dimensional cluster real estate, and record the adjacency relationship between the real estate unit and the surrounding real estate units;

Step 3: Select one or more real estate units of interest, calculate the translation vector, zoom size and rotation angle of other real estate units according to the deformation function, and then perform affine transformation to highlight the real estate units of interest;

Step 4: According to the recorded adjacency relationship, connect the center points of the "adjacent" surfaces to express the actual spatial relationship.

The advantage of the present invention is that it proposes a cluster-oriented three-dimensional real estate visualization method, which can clearly display the internal real estate units that originally have visual barriers, and the effect is obvious, and it is based on the real estate plane structure diagram to meet the actual visualization needs.

Description of drawings

Fig. 1 is the system flowchart of the embodiment of the present invention;

Fig. 2 is a schematic diagram of an orthogonal deformation function of a clustered three-dimensional real estate according to an embodiment of the present invention.

Specific implementation method

In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

Please see Fig. 1, a kind of cluster-oriented discrete analysis method for three-dimensional real estate provided by the present invention comprises the following steps:

Step 1: Generate clustered three-dimensional real estate according to the floor plan, floor height and number of floors of the real estate;

Step 2: uniquely identify the information of each real estate unit in the three-dimensional cluster real estate, and record the adjacency relationship between the real estate unit and the surrounding real estate units;

According to the sorting information of the position of the real estate unit in the three-dimensional space of the cluster, the unique information of the index identification of multiple coordinate dimensions is generated to represent the ID number of the real estate unit, which is called an identifier; the identifier indirectly expresses the position of the real estate unit in three dimensions , the adjacency information between real estate units and other real estate units is recorded in the form of triplets, because the adjacency between bodies in 3D is mainly expressed by the adjacency of surfaces, and in clustered 3D real estate, a house unit will be connected to multiple surrounding house units There is an adjacency relationship, so when recording the adjacency relationship between housing units, it is expanded from the plane level. The format of the triplet is as follows:

Object1:<index1, Object2_id, index2>

Among them, Object1 is the current real estate unit, index1 is the index of the adjacent face in the current real estate unit, Object2_id is the identifier of the adjacent real estate unit, and index2 is the index of the adjacent face in the adjacent real estate unit.

Step 3: Select one or more real estate units of interest, calculate the translation vector, zoom size and rotation angle of other real estate units according to the deformation function, and then perform affine transformation to make the real estate units of interest stand out; Shifting is the discretization of the clustered 3D real estate as a whole according to a certain distance and direction, and the real estate units of interest cannot be highlighted.

Choosing different deformation transformation functions will achieve different visual effects. The original intention is to display the internal object characteristics of the cluster object through the function. This example lists the orthogonal transformation functions for illustration. The deformation transformation functions have different focuses in design, and will be discussed in The spatial characteristics are different, and the orthogonal function is more prominent in the right-angled shape of the real estate shape.

The deformation function adopted in this embodiment is an orthogonal deformation function, and the calculation formula is as follows:

t _x , t _y , t _z are user-defined deformation distances in the X, Y, and Z directions respectively, (x _a , y _a , z _a ) are the center coordinates of the real estate unit of interest; assume that B is other real estate units Any real estate unit, then (x _b , y _b , z _b ) is the center coordinates of B, T=[T _x ,T _y ,T _z ] means the moving vector of B calculated by the orthogonal deformation function, other real estate The unit performs calculation and transformation in turn.

The clustered 3D real estate after the deformation transformation function, when observing the clustered 3D real estate from the outside, the interested housing units can easily observe that the free space around the interested unit is much higher than other units, and the probability of line of sight being blocked Smaller, it is easy to observe its geometric information and topological relationship; at the same time, the deformed cluster object still maintains the vertical and orthogonal relationship between housing units.

Step 4: According to the recorded adjacency relationship, connect the center points of the "adjacent" surfaces to express the actual spatial relationship.

Figure 2 is an illustration of an example of the orthogonal deformation function of clustered three-dimensional real estate. (a) is a real estate plane structure diagram, using regular real estate units for illustration, this technical solution is also applicable to polygonal or complex plane data; (b) is a three-dimensional cluster generated according to the real estate plane structure diagram, floor height and floor number elevation At this time, it is not easy to observe the geometric characteristics and topological relationship of the internal real estate units; (c) is the clustered three-dimensional real estate after equidistant offset; (d) and (e) are the clustered three-dimensional real estate after the deformation function transformation, from the outside When observing clustered three-dimensional real estate, the interested housing units can be easily observed, among which the dark real estate units are the units that the user is interested in, and the free space around the interested units is much higher than other units, which can be easily observed Its geometric information and topological relationship; at the same time, the deformed cluster objects still maintain the vertical and orthogonal relationship between housing units.

It should be understood that the parts not described in detail in this specification belong to the prior art.

It should be understood that the above-mentioned descriptions for the preferred embodiments are relatively detailed, and should not therefore be considered as limiting the scope of the patent protection of the present invention. Within the scope of protection, replacements or modifications can also be made, all of which fall within the protection scope of the present invention, and the scope of protection of the present invention should be based on the appended claims.

Claims (3)

1. A method for discrete analysis of cluster-oriented three-dimensional real estate, characterized in that, comprising the following steps: Step 1: Generate clustered three-dimensional real estate according to the floor plan, floor height and number of floors of the real estate; Step 2: uniquely identify the information of each real estate unit in the three-dimensional cluster real estate, and record the adjacency relationship between the real estate unit and the surrounding real estate units; Step 3: Select one or more real estate units of interest, calculate the translation vector, zoom size and rotation angle of other real estate units according to the deformation function, and then perform affine transformation to highlight the real estate units of interest; Step 4: According to the recorded adjacency relationship, connect the center points of the "adjacent" surfaces to express the actual spatial relationship. 2. the discrete analysis method facing cluster three-dimensional real estate according to claim 1, is characterized in that, the specific realization process of step 2 is: according to the size sorting information of real estate unit in cluster three-dimensional space position, generate the coordinate dimension of a plurality of The index identifies unique information, which is used to represent the ID number of the real estate unit, which is called an identifier; the identifier indirectly expresses the position of the real estate unit in three dimensions, and uses triplets to record the adjacency information between the real estate unit and other real estate units: Object1:<index1, Object2_id, index2> Among them, Object1 is the current real estate unit, index1 is the index of the adjacent face in the current real estate unit, Object2_id is the identifier of the adjacent real estate unit, and index2 is the index of the adjacent face in the adjacent real estate unit. 3. According to the discrete analysis method facing cluster three-dimensional real estate described in claim 1, it is characterized in that the deformation function described in step 3 is an orthogonal deformation function, and the calculation formula is as follows: <mrow><msub><mi>T</mi><mi>x</mi></msub><mo>=</mo><mfencedopen="{"close=""><mtable><mtr><mtd><mn>0</mn></mtd><mtd><mrow><mi>i</mi><mi>f</mi></mrow></mtd><mtd><mrow><msub><mi>x</mi><mi>b</mi></msub><mo>=</mo><msub><mi>x</mi><mi>a</mi></msub></mrow></mtd></mtr><mtr><mtd><mrow><mo>-</mo><msub><mi>t</mi><mi>x</mi></msub></mrow></mtd><mtd><mrow><mi>i</mi><mi>f</mi></mrow></mtd><mtd><mrow><msub><mi>x</mi><mi>b</mi></msub><mo>&gt;</mo><msub><mi>x</mi><mi>a</mi></msub></mrow></mtd></mtr><mtr><mtd><msub><mi>t</mi><mi>x</mi></msub></mtd><mtd><mrow><mi>i</mi><mi>f</mi></mrow></mtd><mtd><mrow><msub><mi>x</mi><mi>b</mi></msub><mo>&lt;</mo><msub><mi>x</mi><mi>a</mi></msub></mrow></mtd></mtr></mtable></mfenced></mrow> <mrow><msub><mi>T</mi><mi>y</mi></msub><mo>=</mo><mfencedopen="{"close=""><mtable><mtr><mtd><mn>0</mn></mtd><mtd><mrow><mi>i</mi><mi>f</mi></mrow></mtd><mtd><mrow><msub><mi>y</mi><mi>b</mi></msub><mo>=</mo><msub><mi>y</mi><mi>a</mi></msub></mrow></mtd></mtr><mtr><mtd><mrow><mo>-</mo><msub><mi>t</mi><mi>y</mi></msub></mrow></mtd><mtd><mrow><mi>i</mi><mi>f</mi></mrow></mtd><mtd><mrow><msub><mi>y</mi><mi>b</mi></msub><mo>&gt;</mo><msub><mi>y</mi><mi>a</mi></msub></mrow></mtd></mtr><mtr><mtd><msub><mi>t</mi><mi>y</mi></msub></mtd><mtd><mrow><mi>i</mi><mi>f</mi></mrow></mtd><mtd><mrow><msub><mi>y</mi><mi>b</mi></msub><mo>&lt;</mo><msub><mi>y</mi><mi>a</mi></msub></mrow></mtd></mtr></mtable></mfenced></mrow> <mrow><msub><mi>T</mi><mi>z</mi></msub><mo>=</mo><mfencedopen="{"close=""><mtable><mtr><mtd><mn>0</mn></mtd><mtd><mrow><mi>i</mi><mi>f</mi></mrow></mtd><mtd><mrow><msub><mi>z</mi><mi>b</mi></msub><mo>=</mo><msub><mi>z</mi><mi>a</mi></msub></mrow></mtd></mtr><mtr><mtd><mrow><mo>-</mo><msub><mi>t</mi><mi>z</mi></msub></mrow></mtd><mtd><mrow><mi>i</mi><mi>f</mi></mrow></mtd><mtd><mrow><msub><mi>z</mi><mi>b</mi></msub><mo>&gt;</mo><msub><mi>z</mi><mi>a</mi></msub></mrow></mtd></mtr><mtr><mtd><msub><mi>t</mi><mi>z</mi></msub></mtd><mtd><mrow><mi>i</mi><mi>f</mi></mrow></mtd><mtd><mrow><msub><mi>z</mi><mi>b</mi></msub><mo>&lt;</mo><msub><mi>z</mi><mi>a</mi></msub></mrow></mtd></mtr></mtable></mfenced></mrow> t _x , t _y , t _z are user-defined deformation distances in the X, Y, and Z directions respectively, (x _a , y _a , z _a ) are the center coordinates of the real estate unit of interest; assume that B is other real estate units Any real estate unit, then (x _b , y _b , z _b ) is the center coordinates of B, T=[T _x ,T _y ,T _z ] means the moving vector of B calculated by the orthogonal deformation function, other real estate The unit performs calculation and transformation in sequence.