CN109344750B - Complex structure three-dimensional object identification method based on structure descriptor - Google Patents
Complex structure three-dimensional object identification method based on structure descriptor Download PDFInfo
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Abstract
The invention discloses a complex structure three-dimensional object identification method based on a structure descriptor, which comprises the steps of extracting point cloud units corresponding to all parts in a target real three-dimensional point cloud, constructing a standard three-dimensional structure descriptor by adopting the size of an enclosure box of the point cloud unit corresponding to each part, the distance between the center of a core point cloud unit and the centers of other point cloud units and the included angle formed by connecting lines of the centers of each pair of point cloud units and the center of the core point cloud unit, segmenting a three-dimensional scene to be identified to obtain all point cloud units in the three-dimensional scene, constructing a candidate three-dimensional structure descriptor, calculating the Manhattan distance between the candidate three-dimensional structure descriptor and the standard structure descriptor, and if the distance is smaller than a set second threshold value, determining that the point cloud unit corresponding to the candidate three-dimensional structure descriptor is an object to be identified. The method has low calculation consumption, and can realize rapid and accurate identification of the three-dimensional object with the complex structure in the three-dimensional point cloud scene.
Description
Technical Field
The invention belongs to the technical field of computer vision, and particularly relates to a complex-structure three-dimensional object identification method based on a structure descriptor.
Background
Object recognition in complex scenes is a very important research area for computer vision. Two-dimensional object recognition has been extensively studied over the past few decades and has become a relatively mature field. The three-dimensional point cloud may provide more geometric information than the two-dimensional image, and thus estimating the pose of the object in the three-dimensional point cloud is more accurate than estimating the pose in the two-dimensional image.
The three-dimensional object descriptor based on the point cloud is a popular research, and the aim of the three-dimensional object descriptor is to achieve the aim of identifying the point cloud by describing the local part or the whole of the point cloud. The existing three-dimensional point cloud descriptor can effectively identify a single target, but cannot be applied to identification of three-dimensional objects with complex structures in a three-dimensional point cloud scene. In the process of segmenting and identifying the three-dimensional scene, a three-dimensional object with a complex structure is often segmented into a plurality of point clouds, and the point cloud descriptor which can only be applied to a single point cloud at this time is invalid.
How to identify the three-dimensional object with the complex structure is still a problem which needs to be solved urgently in the industry.
Disclosure of Invention
The invention provides a complex structure three-dimensional object identification method based on a structure descriptor, which solves the problem that the existing three-dimensional descriptor can not identify a complex structure three-dimensional object and can effectively identify the complex structure three-dimensional object in a three-dimensional point cloud scene.
A structure descriptor-based complex structure three-dimensional object recognition method comprises the following steps:
extracting point cloud units corresponding to all parts in the target real three-dimensional point cloud, and calculating to obtain the size and the center of an enclosure box of the point cloud unit corresponding to each part;
selecting one point cloud unit from the point cloud units corresponding to the components as a core point cloud unit, and calculating the distance from the center of the core point cloud unit to the centers of other point cloud units;
pairing other point cloud units except the core point cloud unit in pairs, and calculating an included angle formed by connecting the center of each pair of point cloud units with the center of the core point cloud unit;
constructing a standard three-dimensional structure descriptor by adopting the size of an enclosure box of a point cloud unit corresponding to each component, the distance from the center of a core point cloud unit to the centers of other point cloud units and an included angle formed by connecting the center of each pair of point cloud units and the center of the core point cloud unit;
dividing a three-dimensional scene to be identified to obtain all point cloud units in the three-dimensional scene, and screening out all point cloud units with the number of points greater than a first threshold;
traversing the screened point cloud units, taking any point cloud unit as a core point cloud unit, optionally selecting n point cloud units in a three-dimensional neighborhood of the core point cloud unit, and constructing a candidate three-dimensional structure descriptor, wherein n is equal to the number of the point cloud units corresponding to each part in the target real three-dimensional point cloud minus one;
and calculating the Manhattan distance between the candidate three-dimensional structure descriptor and the standard structure descriptor, and if the distance is smaller than a set second threshold, the point cloud unit corresponding to the candidate three-dimensional structure descriptor is the object to be identified.
Further, the constructing of the candidate three-dimensional structure descriptor includes:
calculating the size and the center of a bounding box of the core point cloud unit and n point cloud units selected in the three-dimensional neighborhood of the core point cloud unit;
calculating the distance from the center of the core point cloud unit to the centers of other point cloud units;
pairing other point cloud units except the core point cloud unit in pairs, and calculating an included angle formed by connecting the center of each pair of point cloud units with the center of the core point cloud unit;
and constructing a candidate three-dimensional structure descriptor by adopting the size of the bounding box of each point cloud unit, the distance from the center of the core point cloud unit to the centers of other point cloud units and an included angle formed by connecting the center of each pair of point cloud units with the center of the core point cloud unit.
The invention provides a three-dimensional structure descriptor based complex structure three-dimensional object recognition method, and aims to recognize complex structure three-dimensional objects in a three-dimensional reconstruction scene by describing the relation of each part of the complex structure three-dimensional object. The bounding box information of the object parts, the distance between the parts and the included angle combination are used as identification information, the calculation consumption is low, and the rapid and accurate identification of the three-dimensional object with the complex structure can be realized in the three-dimensional point cloud scene.
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FIG. 1 is a flow chart of a complex structure three-dimensional object recognition method based on a structure descriptor according to the present invention;
FIG. 2 is an example of a standard three-dimensional structure descriptor construction according to the present invention;
fig. 3 is a three-dimensional object recognition example of a complex structure according to the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the drawings and examples, which should not be construed as limiting the present invention.
As shown in fig. 1, the method for identifying a three-dimensional object with a complex structure based on a structure descriptor in this embodiment includes:
and step S1, extracting point cloud units corresponding to all parts in the target real three-dimensional point cloud, and calculating to obtain the size and the center of the bounding box of the point cloud unit corresponding to each part.
The technical scheme is applied to the identification of the three-dimensional object with the complex structure, such as the identification of a machined part. In factory production, a plurality of parts may be mixed together, and three-dimensional object recognition is required to select a desired kind.
In the embodiment, a three-dimensional object to be identified is called a target, and a standard three-dimensional structure descriptor is constructed by using a target real three-dimensional point cloud.
In the example shown in fig. 2, the complex structured three-dimensional object (target) is composed of four parts, parts 1, 2, 3, 4 respectively. Fig. 2 shows the point cloud units corresponding to the components 1, 2, 3, 4, and the corresponding point cloud units are also described below with reference to the components 1, 2, 3, 4.
In the step, bounding boxes of the point cloud units corresponding to the components are calculated, and length, width and height information and center point coordinates of the components are obtained, wherein the center points of the four components are B, A, C, O respectively.
And step S2, selecting one point cloud unit from the point cloud units corresponding to the components as a core point cloud unit, and calculating the distance from the center of the core point cloud unit to the centers of other point cloud units.
In the step, one point cloud unit is selected from the point cloud units corresponding to all the parts as a core point cloud unit, and the part 4 is assumed as the core point cloud, and the distances from the parts 1, 2 and 3 to the core point cloud unit are calculated respectively, namely the distances between OB, OA and OC are calculated.
And step S3, pairing other point cloud units except the core point cloud unit, and calculating an included angle formed by connecting the center of each pair of point cloud units with the center of the core point cloud unit.
In the step, except for a component 4, the rest point cloud units are paired pairwise, and an included angle formed by each pair of the point cloud unit center and the component 4 point cloud unit center is calculated, wherein the included angle is angle AOC, angle BOC and angle AOB.
And step S4, constructing a standard three-dimensional structure descriptor by adopting the size of the bounding box of the point cloud unit corresponding to each component, the distance from the center of the core point cloud unit to the centers of other point cloud units and the included angle formed by the connecting lines of each pair of point cloud unit centers and the center of the core point cloud unit.
And linearly combining the size information of the bounding box, the distance between the centers of the point cloud units and the angle formed by the connecting lines between the centers of the point cloud units to obtain the standard three-dimensional structure descriptor.
Specifically, the size data of the bounding box is multiplied by a weight a1And sorting the bounding boxes from large to small according to the length to obtain 3n (each bounding box comprises 3-dimensional data of length, width and height, and n is the number of point cloud units except the core point cloud unit) dimensional data.
Multiplying the distance from the center of each point cloud unit to the center of the core point cloud unit by a1And ordering from large to small to obtain n-dimensional data.
Sequencing the connecting lines of each pair of point cloud unit centers and the core point cloud unit centers from large to small to obtain
Dimension data.
Combining the three data to obtain one
A standard three-dimensional structural descriptor of a dimension. In this embodiment, the target is composed of four parts, n being equal to 3.
And step S5, segmenting the three-dimensional scene to be identified to obtain all point cloud units in the three-dimensional scene, and screening out all point cloud units with the number of points greater than a first threshold.
In this embodiment, a three-dimensional scene to be recognized is segmented, and three-dimensional point cloud segmentation is already a relatively mature technology, and is not described herein again.
And after segmentation, obtaining all point cloud units in the three-dimensional scene, and screening out all point cloud units with the number of points greater than a first threshold. In this embodiment, the first threshold is used to remove the influence of noise and interference, and the point cloud units with the number less than or equal to the first threshold are removed.
For example, if the first threshold is 500, the number of points in the screened point cloud units is greater than 500, so that noise point clouds with the number of points of 500 or less are removed.
And step S6, traversing the screened point cloud units, selecting n point cloud units in a three-dimensional neighborhood of any point cloud unit as a core point cloud unit, and constructing a candidate three-dimensional structure descriptor, wherein n is equal to the number of the point cloud units corresponding to each part in the target real three-dimensional point cloud minus one.
In this embodiment, a candidate three-dimensional structure descriptor is constructed, that is, any point cloud unit is a core point cloud unit, n point cloud units are optionally selected in a three-dimensional neighborhood of the point cloud unit, then the size of an enclosure box of each point cloud unit, the distance from the center of the core point cloud unit to the centers of other point cloud units, and an included angle formed by connecting the center of each pair of point cloud units and the center of the core point cloud unit are calculated according to the methods of steps S1 to S3, and a three-dimensional structure descriptor is constructed according to the method of step S4.
Namely:
calculating the size and the center of a bounding box of the core point cloud unit and n point cloud units selected in the three-dimensional neighborhood of the core point cloud unit;
calculating the distance from the center of the core point cloud unit to the centers of other point cloud units;
pairing other point cloud units except the core point cloud unit in pairs, and calculating an included angle formed by connecting the center of each pair of point cloud units with the center of the core point cloud unit;
and constructing a candidate three-dimensional structure descriptor by adopting the size of the bounding box of each point cloud unit, the distance from the center of the core point cloud unit to the centers of other point cloud units and an included angle formed by connecting the center of each pair of point cloud units with the center of the core point cloud unit.
The three-dimensional structure descriptor constructed in the step is a candidate three-dimensional structure descriptor, for example, as shown in fig. 3, and if 6 screened point cloud units are provided, the point cloud unit 1 is used as a core point cloud unit, and 3 point cloud units are selected from 2 to 6 to construct; then, constructing by taking the point cloud unit 2 as a core point cloud unit and optionally selecting 3 point cloud units from 1, 3-6; and the description is omitted until all the point cloud units are traversed.
Step S7, calculating the Manhattan distance between the candidate three-dimensional structure descriptor and the standard structure descriptor, and if the distance is smaller than a set second threshold, the point cloud unit corresponding to the candidate three-dimensional structure descriptor is the object to be identified.
In this embodiment, the manhattan distance between the three-dimensional structure descriptor and the standard structure descriptor is calculated, and if the manhattan distance is smaller than a second threshold, for example, 30, the object composed of the current core point cloud and the n point cloud units is the object to be identified.
In this embodiment, it is assumed that a point cloud unit corresponding to the component 4 is used as a core point cloud unit, the components 1, 2, and 3 are selected from the components 2 to 6 to construct a candidate three-dimensional structure descriptor, and the manhattan distance between the candidate three-dimensional structure descriptor and the standard structure descriptor is found to be less than 30 through calculation, so that the three-dimensional point cloud composed of the component 4 and the components 1, 2, and 3 is an object to be identified.
It should be noted that, in this embodiment, the weight a1The first threshold value and the second threshold value are designed according to the point cloud units corresponding to all parts in the target real three-dimensional point cloud and the experimental structure, and a good identification effect can be achieved. Meanwhile, it should be noted that the technical solution is not limited to target recognition of 4 components, and has a better recognition capability for a three-dimensional object with a complex structure.
The above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and those skilled in the art can make various corresponding changes and modifications according to the present invention without departing from the spirit and the essence of the present invention, but these corresponding changes and modifications should fall within the protection scope of the appended claims.
Claims (2)
1. A structure descriptor-based complex structure three-dimensional object recognition method is characterized by comprising the following steps:
extracting point cloud units corresponding to all parts in the target real three-dimensional point cloud, and calculating to obtain the size and the center of an enclosure box of the point cloud unit corresponding to each part;
selecting one point cloud unit from the point cloud units corresponding to the components as a core point cloud unit, and calculating the distance from the center of the core point cloud unit to the centers of other point cloud units;
pairing other point cloud units except the core point cloud unit in pairs, and calculating an included angle formed by connecting the center of each pair of point cloud units with the center of the core point cloud unit;
constructing a standard three-dimensional structure descriptor by adopting the size of an enclosure box of a point cloud unit corresponding to each component, the distance from the center of a core point cloud unit to the centers of other point cloud units and an included angle formed by connecting the center of each pair of point cloud units and the center of the core point cloud unit;
dividing a three-dimensional scene to be identified to obtain all point cloud units in the three-dimensional scene, and screening out all point cloud units with the number of points greater than a first threshold;
traversing the screened point cloud units, taking any point cloud unit as a core point cloud unit, optionally selecting n point cloud units in a three-dimensional neighborhood of the core point cloud unit, and constructing a candidate three-dimensional structure descriptor, wherein n is equal to the number of the point cloud units corresponding to each part in the target real three-dimensional point cloud minus one;
and calculating the Manhattan distance between the candidate three-dimensional structure descriptor and the standard structure descriptor, and if the distance is smaller than a set second threshold, the point cloud unit corresponding to the candidate three-dimensional structure descriptor is the object to be identified.
2. The method for identifying a three-dimensional object with a complex structure based on a structure descriptor as claimed in claim 1, wherein the constructing the candidate three-dimensional structure descriptor comprises:
calculating the size and the center of a bounding box of the core point cloud unit and n point cloud units selected in the three-dimensional neighborhood of the core point cloud unit;
calculating the distance from the center of the core point cloud unit to the centers of other point cloud units;
pairing other point cloud units except the core point cloud unit in pairs, and calculating an included angle formed by connecting the center of each pair of point cloud units with the center of the core point cloud unit;
and constructing a candidate three-dimensional structure descriptor by adopting the size of the bounding box of each point cloud unit, the distance from the center of the core point cloud unit to the centers of other point cloud units and an included angle formed by connecting the center of each pair of point cloud units with the center of the core point cloud unit.
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