CN116721036A - Noise point judging method, device, electronic equipment and readable storage medium - Google Patents
Noise point judging method, device, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The disclosure provides a noise point judging method, a device, electronic equipment and a readable storage medium. The method comprises the following steps: respectively acquiring the number of projection points of the three-dimensional point cloud of each three-dimensional subspace in each sub-judgment area, wherein each three-dimensional subspace corresponds to a plurality of phase spaces divided by the whole phase diagram, and the three-dimensional point cloud of the surface of the measured object acquired based on the whole phase diagram is divided into each three-dimensional subspace; constructing at least one communication area of the sub-judgment areas based on the number of projection points of the three-dimensional point cloud of each three-dimensional subspace in each sub-judgment area of the plurality of sub-judgment areas of the noise point judgment plane area, wherein the noise point judgment plane area is a plane area perpendicular to the phase increment direction of the plurality of phase partitions along the whole phase map; and judging the three-dimensional point cloud corresponding to the projection points in the sub-judgment areas outside the communication area as noise points.
Description
Technical Field
The present disclosure relates to a noise point determination method, a noise point determination device, an electronic apparatus, and a readable storage medium.
Background
In applications where it is desirable to use 3D vision to guide an industrial robot to grasp, a high quality point cloud can reduce the difficulty of subsequent recognition, matching, and positioning.
In practical applications, the point cloud is inevitably noisy, so that filtering processing is required to be performed on the point cloud to improve the signal-to-noise ratio of the point cloud.
At present, the point cloud filtering is mainly realized according to the characteristics of the point cloud. For example, many filtering functions aiming at unordered point clouds are provided in the PCL library, and the filtering functions are general and applicable to all point clouds, and can be used for realizing point cloud filtering meeting the requirements of specific applications by adjusting relevant parameters of unordered point cloud filtering functions in specific application scenes.
In the three-dimensional reconstruction based on the phase shift gray code, a plurality of phase shift gray code patterns are projected on a measured object, the phase shift gray code patterns reflected by the surface of the measured object are shot, then a main value phase diagram and a fringe sequence diagram are obtained through calculation, an absolute phase diagram is obtained based on the main value phase diagram and the fringe sequence diagram, and finally the depth of each point on the measured object is determined based on the absolute phase diagram and camera parameters calibrated in advance, so that point cloud data capable of representing the three-dimensional morphological characteristics of the measured object is obtained.
When noise exists in the point cloud data, the three-dimensional reconstruction accuracy of the measured object is reduced. Therefore, in three-dimensional reconstruction based on phase-shifted gray codes, it is important to remove noise in the point cloud data.
Disclosure of Invention
The disclosure provides a noise point judging method, a device, electronic equipment and a readable storage medium.
According to one aspect of the present disclosure, there is provided a noise point determination method including:
respectively acquiring the number of projection points of the three-dimensional point cloud of each three-dimensional subspace in each sub-judgment area, wherein each three-dimensional subspace corresponds to a plurality of phase spaces divided by the integral phase map, and the three-dimensional point cloud of the surface of the measured object acquired based on the integral phase map is divided into each three-dimensional subspace;
constructing at least one communication region of sub-judgment regions based on the number of projection points of three-dimensional point clouds of each three-dimensional subspace in each of a plurality of sub-judgment regions of the noise point judgment plane region, wherein the noise point judgment plane region is a plane region perpendicular to the phase increment direction of the plurality of phase partitions along the whole phase map; and
and judging the three-dimensional point cloud corresponding to the projection points in the sub-judgment areas outside the communication area as noise points.
The noise point determination method according to at least one embodiment of the present disclosure further includes:
and respectively projecting the three-dimensional point clouds in each three-dimensional subspace to the noise point judging plane area to obtain two-dimensional projections of the point clouds of each three-dimensional subspace in the noise point judging plane area, and respectively judging noise points based on the distribution characteristics of the point clouds of each three-dimensional subspace in the noise point judging plane area.
According to the noise point determination method of at least one embodiment of the present disclosure, the plurality of sub-determination areas cover the entire noise point determination plane area.
According to a noise point determination method of at least one embodiment of the present disclosure, the configuration of the noise point determination plane area includes:
the sizes of the respective sub-determination areas in the first direction are configured to be the same;
the dimensions of the respective sub-determination areas in a second direction perpendicular to the first direction are configured to be positively correlated with a working distance of a camera for acquiring a three-dimensional point cloud of the surface of the object under test.
According to the noise point determination method of at least one embodiment of the present disclosure, the first direction is a depth direction of the three-dimensional point cloud, and the first direction, the second direction, and the phase increasing direction are perpendicular to each other.
According to the noise point determination method of at least one embodiment of the present disclosure, the distribution characteristics of the point cloud of each three-dimensional subspace in the noise point determination plane region include the projection point number characteristics in each sub-determination region.
According to the noise point determination method of at least one embodiment of the present disclosure, if a certain sub-determination area has a projected point and there is at least one neighboring sub-determination area having a projected point, the sub-determination area is placed in a connected area.
The noise point determination method according to at least one embodiment of the present disclosure further includes:
and secondarily judging the point clouds corresponding to the projection points in the sub-judgment areas with the number of the projection points in the communication area being smaller than or equal to the threshold value as noise points.
A noise point determination method according to at least one embodiment of the present disclosure is characterized in that a boundary of a measured object can be divided into different sub-determination areas.
According to another aspect of the present disclosure, there is provided a noise point determination apparatus including:
the acquisition module is used for respectively acquiring the number of projection points of the three-dimensional point cloud of each three-dimensional subspace in each sub-judgment area, each three-dimensional subspace corresponds to a plurality of phase spaces divided by the integral phase diagram, and the three-dimensional point cloud of the surface of the measured object acquired based on the integral phase diagram is divided into each three-dimensional subspace;
The construction module is used for constructing at least one communication area of the sub-judgment areas based on the number of projection points of the three-dimensional point cloud of each three-dimensional subspace in each sub-judgment area of the noise point judgment plane area, wherein the noise point judgment plane area is a plane area perpendicular to the phase increment direction of the plurality of phase partitions along the whole phase diagram; and
and the judging module judges the three-dimensional point cloud corresponding to the projection points in the sub-judging areas outside the communication area as noise points.
According to still another aspect of the present disclosure, there is provided an electronic apparatus including:
a memory storing execution instructions; and
a processor executing the execution instructions stored in the memory, causing the processor to perform the noise point determination method as described in any one of the above.
According to still another aspect of the present disclosure, there is provided a readable storage medium having stored therein execution instructions which when executed by a processor are to implement the noise point determination method as any one of the above.
According to one aspect of the present disclosure, there is provided a three-dimensional point cloud filtering method, including:
Dividing an overall phase map obtained based on a projection map projected onto a surface of a measured object into a plurality of continuous phase partitions so as to divide a three-dimensional point cloud of the surface of the measured object obtained based on the overall phase map into three-dimensional subspaces corresponding to the phase partitions;
projecting the three-dimensional point clouds in each three-dimensional subspace to a noise point judging plane area respectively to obtain two-dimensional projections of the point clouds of each three-dimensional subspace in the noise point judging plane area;
noise points are determined based on the distribution characteristics of the point clouds of the three-dimensional subspaces in the noise point determination plane area respectively so as to remove the noise points.
According to the three-dimensional point cloud filtering method of at least one embodiment of the present disclosure, the plurality of phase partitions are arranged along a phase increasing direction of the overall phase map, and the noise point determination plane area is a plane area perpendicular to the phase increasing direction.
According to the three-dimensional point cloud filtering method of at least one embodiment of the present disclosure, the global phase map is equally divided to obtain each phase partition.
According to the three-dimensional point cloud filtering method of at least one embodiment of the present disclosure, the overall phase map is equally divided based on the phase period characteristics of the overall phase map.
According to the three-dimensional point cloud filtering method of at least one embodiment of the present disclosure, each of the phase partitions has the same interval length.
According to a three-dimensional point cloud filtering method of at least one embodiment of the present disclosure, projecting three-dimensional point clouds in respective three-dimensional subspaces to a noise point determination plane area, respectively, to obtain two-dimensional projections of the point clouds of the respective three-dimensional subspaces in the noise point determination plane area, includes:
the noise point determination plane area is configured as a noise point determination plane area having a plurality of sub-determination areas that cover the entire noise point determination plane area.
According to the three-dimensional point cloud filtering method of at least one embodiment of the present disclosure, the configuration of the noise point determination plane area includes:
the sizes of the respective sub-determination areas in the first direction are configured to be the same;
the dimensions of the respective sub-determination areas in a second direction perpendicular to the first direction are configured to be positively correlated with a working distance of a camera for acquiring a three-dimensional point cloud of the surface of the object under test.
According to the three-dimensional point cloud filtering method of at least one embodiment of the present disclosure, the first direction is a depth direction of the three-dimensional point cloud, and the first direction, the second direction, and the phase increasing direction are perpendicular to each other.
According to the three-dimensional point cloud filtering method of at least one embodiment of the present disclosure, the distribution characteristics of the point clouds of the respective three-dimensional subspaces in the noise point determination plane region include the projection point number characteristics in the respective sub-determination regions.
According to at least one embodiment of the present disclosure, a three-dimensional point cloud filtering method for determining a noise point based on a distribution characteristic of a point cloud of each three-dimensional subspace in the noise point determination plane region includes:
for each three-dimensional subspace, respectively acquiring the number of projection points of the point cloud of the three-dimensional subspace in each corresponding sub-judgment region;
constructing at least one communication region of sub-judgment regions based on the number of projection points of the point cloud of the three-dimensional subspace in each sub-judgment region of the noise point judgment plane region;
and judging the point cloud corresponding to the projection points in the sub-judgment areas outside the communication area as noise points.
According to a three-dimensional point cloud filtering method of at least one embodiment of the present disclosure, at least one connected region of sub-determination regions is constructed based on the number of projection points of a point cloud of each three-dimensional subspace within each sub-determination region of the noise point determination plane region, including:
If a certain sub-judgment area has a projection point, and at least one adjacent sub-judgment area with the projection point exists in the sub-judgment area, the sub-judgment area is placed in the communication area.
The three-dimensional point cloud filtering method according to at least one embodiment of the present disclosure further includes:
and secondarily judging the point clouds corresponding to the projection points in the sub-judgment areas with the number of the projection points in the communication area being smaller than or equal to the threshold value as noise points.
According to the three-dimensional point cloud filtering method of at least one embodiment of the present disclosure, a projection image projected onto a surface of an object to be measured includes a gray code image and a phase shift code image, and the whole phase image is obtained based on the following steps:
decoding the Gray code diagram to obtain a stripe sequence number diagram, and decoding the phase shift code diagram to obtain a main value phase diagram;
and obtaining the whole phase diagram based on the main value phase diagram and the fringe sequence number diagram.
According to the three-dimensional point cloud filtering method of at least one embodiment of the present disclosure, the number of phase partitions is determined based on the number of stripes of the gray code pattern projected onto the surface of the measured object, so that the interval length of the phase partitions is adapted to the phase shift period corresponding to each stripe.
According to another aspect of the present disclosure, there is provided a three-dimensional reconstruction method based on a three-dimensional point cloud, including:
obtaining an overall phase diagram of the surface of the measured object based on the projection diagram projected to the surface of the measured object;
acquiring a three-dimensional point cloud representing the surface morphology features of the measured object based on the integral phase diagram;
performing filtering processing on the three-dimensional point cloud to remove noise points in the three-dimensional point cloud;
performing three-dimensional reconstruction of the surface morphological characteristics of the measured object based on the three-dimensional point cloud after removing the noise points;
wherein filtering processing is performed on the three-dimensional point cloud to remove noise points in the three-dimensional point cloud is implemented based on the three-dimensional point cloud filtering method of any one of the embodiments of the present disclosure.
According to still another aspect of the present disclosure, there is provided a three-dimensional point cloud filtering apparatus including:
the dividing module divides an overall phase map obtained based on a projection map projected onto the surface of the object to be measured into a plurality of continuous phase partitions so as to divide a three-dimensional point cloud of the surface of the object to be measured obtained based on the overall phase map into three-dimensional subspaces corresponding to the phase partitions;
the point cloud projection module is used for respectively projecting the three-dimensional point clouds in each three-dimensional subspace to the noise point judgment plane area so as to obtain the two-dimensional projection of the point clouds of each three-dimensional subspace in the noise point judgment plane area;
And the noise point judging module is used for judging noise points based on the distribution characteristics of the point clouds of the three-dimensional subspaces in the noise point judging plane area respectively so as to remove the noise points.
According to still another aspect of the present disclosure, there is provided a three-dimensional reconstruction apparatus based on a three-dimensional point cloud, including:
the overall phase diagram acquisition module is used for acquiring an overall phase diagram of the surface of the measured object based on the projection diagram projected onto the surface of the measured object;
the three-dimensional point cloud acquisition module acquires three-dimensional point clouds representing the surface morphology features of the measured object based on the integral phase diagram;
the three-dimensional point cloud filtering module is used for performing filtering processing on the three-dimensional point cloud to remove noise points in the three-dimensional point cloud;
the three-dimensional reconstruction module is used for carrying out three-dimensional reconstruction on the surface morphological characteristics of the measured object based on the three-dimensional point cloud after the noise points are removed;
the three-dimensional point cloud filtering module is the three-dimensional point cloud filtering device.
According to yet another aspect of the present disclosure, there is provided an electronic device including:
A memory storing execution instructions;
a processor executing the execution instructions stored by the memory, causing the processor to perform the three-dimensional point cloud filtering method of any of the embodiments of the present disclosure and/or to perform the three-dimensional reconstruction method of any of the embodiments of the present disclosure.
According to still another aspect of the present disclosure, there is provided a readable storage medium, wherein the readable storage medium has stored therein execution instructions, which when executed by a processor, are for implementing the three-dimensional point cloud filtering method of any one embodiment of the present disclosure and/or implementing the three-dimensional reconstruction method of any one embodiment of the present disclosure.
According to the embodiment of the disclosure, the whole three-dimensional point cloud data is partitioned based on the phase values of the pixel points, and noise filtering based on phase partitioning is performed by projecting the partitioned point cloud to a two-dimensional plane, so that effective filtering of the three-dimensional reconstruction point cloud is realized, the noise of the point cloud can be effectively reduced, and the signal to noise ratio of the point cloud is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosure and together with the description serve to explain the principles of the disclosure.
Fig. 1 is a flow diagram of a three-dimensional point cloud filtering method according to an embodiment of the present disclosure.
Fig. 2 shows a schematic diagram of converging three-dimensional points corresponding to different phases of an overall phase map of an embodiment of the present disclosure in a projection center.
Fig. 3 is a schematic diagram of a dimensional configuration of a sub-determination region in the Z-axis according to an embodiment of the present disclosure.
Fig. 4 is an example diagram of configuring a sub determination area on the YZ plane in one embodiment of the present disclosure.
Fig. 5 is a flowchart of a noise point determination method in the three-dimensional point cloud filtering method according to a preferred embodiment of the present disclosure.
Fig. 6 is a flowchart of a noise point determination method in a three-dimensional point cloud filtering method according to another preferred embodiment of the present disclosure.
Fig. 7 shows a count diagram of projection points in the respective sub-determination regions shown in fig. 4.
Fig. 8 is a flow diagram of a three-dimensional reconstruction method based on a three-dimensional point cloud according to one embodiment of the present disclosure.
Fig. 9 is a block schematic diagram of a three-dimensional point cloud filtering apparatus employing a hardware implementation of a processing system according to one embodiment of the present disclosure.
Fig. 10 is a block schematic diagram of a three-dimensional reconstruction apparatus employing a hardware implementation of a processing system according to one embodiment of the present disclosure.
Description of the reference numerals
1000. Three-dimensional point cloud filter device
1002. Dividing module
1004. Point cloud projection module
1006. Noise point judging module
2000. Three-dimensional reconstruction device
2002. Integral phase diagram acquisition module
2004. Three-dimensional point cloud acquisition module
2006. Three-dimensional point cloud filtering module
2008. Three-dimensional reconstruction module
1100/2100 bus
1200/2200 processor
1300/2300 memory
1400/2400 other circuits.
Detailed Description
The present disclosure is described in further detail below with reference to the drawings and the embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant content and not limiting of the present disclosure. It should be further noted that, for convenience of description, only a portion relevant to the present disclosure is shown in the drawings.
In addition, embodiments of the present disclosure and features of the embodiments may be combined with each other without conflict. The technical aspects of the present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Unless otherwise indicated, the exemplary implementations/embodiments shown are to be understood as providing exemplary features of various details of some ways in which the technical concepts of the present disclosure may be practiced. Thus, unless otherwise indicated, features of the various implementations/embodiments may be additionally combined, separated, interchanged, and/or rearranged without departing from the technical concepts of the present disclosure.
The use of cross-hatching and/or shading in the drawings is typically used to clarify the boundaries between adjacent components. As such, the presence or absence of cross-hatching or shading does not convey or represent any preference or requirement for a particular material, material property, dimension, proportion, commonality between illustrated components, and/or any other characteristic, attribute, property, etc. of a component, unless indicated. In addition, in the drawings, the size and relative sizes of elements may be exaggerated for clarity and/or descriptive purposes. While the exemplary embodiments may be variously implemented, the specific process sequences may be performed in a different order than that described. For example, two consecutively described processes may be performed substantially simultaneously or in reverse order from that described. Moreover, like reference numerals designate like parts.
When an element is referred to as being "on" or "over", "connected to" or "coupled to" another element, it can be directly on, connected or coupled to the other element or intervening elements may be present. However, when an element is referred to as being "directly on," "directly connected to," or "directly coupled to" another element, there are no intervening elements present. For this reason, the term "connected" may refer to physical connections, electrical connections, and the like, with or without intermediate components.
The terminology used herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, when the terms "comprises" and/or "comprising," and variations thereof, are used in the present specification, the presence of stated features, integers, steps, operations, elements, components, and/or groups thereof is described, but the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof is not precluded. It is also noted that, as used herein, the terms "substantially," "about," and other similar terms are used as approximation terms and not as degree terms, and as such, are used to explain the inherent deviations of measured, calculated, and/or provided values that would be recognized by one of ordinary skill in the art.
The three-dimensional point cloud filtering method, the three-dimensional reconstruction method, the apparatus, and the like of the present disclosure are described in detail below with reference to fig. 1 to 10.
Referring to fig. 1, in some embodiments of the present disclosure, a three-dimensional point cloud filtering method S100 of the present disclosure includes:
S102, dividing an overall phase map of the surface of the object to be measured, which is obtained based on a projection map projected onto the surface of the object to be measured, into a plurality of continuous phase partitions so as to divide a three-dimensional point cloud of the surface of the object to be measured, which is obtained based on the overall phase map, into three-dimensional subspaces corresponding to the phase partitions;
s104, respectively projecting the three-dimensional point clouds in each three-dimensional subspace to a noise point judgment plane area to obtain two-dimensional projections of the point clouds of each three-dimensional subspace in the noise point judgment plane area;
s106, respectively judging noise points based on the distribution characteristics of the point clouds of the three-dimensional subspaces in the noise point judging plane area so as to remove the noise points.
According to the three-dimensional point cloud filtering method S100, the whole phase diagram is divided into a plurality of continuous phase partitions, the three-dimensional point cloud is divided into three-dimensional subspaces corresponding to the phase partitions based on the phase partitions, and then the three-dimensional point cloud in the three-dimensional subspaces is projected to the noise point judgment plane area, so that the judgment of noise points is realized based on the distribution characteristics (the correct points in the point cloud and the necessary distribution characteristics of the noise points) of the point cloud in the noise point judgment plane area, and the filtering of the three-dimensional point cloud is realized.
According to the three-dimensional point cloud filtering method, in three-dimensional reconstruction based on phase shift gray codes, a plurality of phase shift gray code patterns can be projected on the surface of a measured object in a current scene by a projection device, after the phase shift gray code patterns reflected by the surface of the measured object in the current scene are shot by a 3D camera, the phase shift gray code patterns reflected by the surface of the measured object in the current scene are calculated to obtain a main value phase diagram and a stripe sequence number diagram, an absolute phase diagram, namely the integral phase diagram described above, is obtained based on the main value phase diagram and the stripe sequence number diagram, and finally depth data of each point on the surface of the measured object is determined based on the absolute phase diagram and camera parameters calibrated in advance, so that point cloud data representing three-dimensional morphological characteristics of the measured object in the current scene are obtained.
Fig. 2 shows a schematic diagram of converging three-dimensional points corresponding to different phases of an overall phase map of an embodiment of the present disclosure in a projection center.
Referring to fig. 2, projection light corresponding to pixel points corresponding to different phases (phase 1, phase 2, phase 3) is converged at a projection center. Each pixel point in the whole phase diagram, for example, a point in a three-dimensional point cloud generated based on three-dimensional reconstruction of a phase shift gray code, corresponding projection light rays are converged at a projection center, the projection light rays corresponding to all pixel points in a certain phase partition form a three-dimensional subspace (thin slice) pointing to the projection center in space, all pixel points corresponding to the same phase value correspond to the same plane in the thin slice, under the condition that noise does not exist, projection points of all pixel points with the same phase value or belonging to the same phase partition on a vertical plane in the phase increasing direction should be intensively distributed in a certain area, and if discrete points exist on the projection points of all pixel points belonging to the same phase partition on the vertical plane in the phase increasing direction, the points in the point cloud corresponding to the discrete points are noise points.
In some embodiments of the present disclosure, the plurality of phase partitions described above of the present disclosure are arranged along a phase increment direction of the overall phase map, and the noise point determination plane area is a plane area perpendicular to the phase increment direction.
In some embodiments of the present disclosure, the entire phase map is equally spaced apart to obtain individual phase partitions.
In some embodiments of the present disclosure, each phase partition described above in the present disclosure has the same interval length.
In some embodiments of the present disclosure, the projection map projected onto the surface of the measured object includes a phase shift encoded map and a gray code map, and the overall phase map is obtained based on the steps of:
decoding the phase shift coded picture to obtain a main value phase picture, and decoding the Gray code picture to obtain a stripe sequence number picture;
and obtaining the whole phase diagram based on the main value phase diagram and the stripe sequence number diagram.
The number of phase partitions described above in the present disclosure may be determined based on the number of stripes of the gray code pattern projected onto the surface of the measured object, so that the interval length of the phase partitions is adapted to the phase shift period corresponding to each stripe.
In some embodiments of the present disclosure, the overall phase map is equally spaced apart based on the phase period characteristics of the overall phase map to obtain individual phase partitions.
In the three-dimensional reconstruction based on the phase-shift gray code, the phase-shift gray code pattern projected to the measured object of the current scene by the projection device comprises a plurality of stripes, a single stripe corresponds to one phase shift period, a certain number of phase partitions can be set according to the number of the stripes in the phase-shift gray code pattern and the phase shift period of the single stripe, and interval length parameters of each phase partition can be configured.
Illustratively, the phase shift gray code pattern has a total of 100 stripes, the phase shift period corresponding to a single stripe is 2PI, the phase partitions may be set to 200, the interval length of each phase partition may be set to PI, and the interval length parameters of the phase partitions may be configured to [0, PI ], [ PI,2PI ], [2PI,3PI ]). Referring again to fig. 2, the projected light corresponding to the pixel points in each phase partition will form a three-dimensional subspace (thin slice) in space.
Under the teaching of the technical scheme of the disclosure, the method for dividing the phase partition, the interval length of the phase partition and the like are adjusted by a person skilled in the art, and all fall into the protection scope of the disclosure.
In some embodiments of the present disclosure, a three-dimensional point cloud of a surface of a measured object of a current scene is segmented along a phase increment direction according to a phase partition to which each pixel point belongs in an overall phase map, so as to obtain a point cloud piece corresponding to each phase partition.
The point cloud pieces are in one-to-one correspondence with the phase partitions, and the point cloud data in each point cloud piece can be obtained according to the coordinates of all pixel points in the phase partition corresponding to the point cloud piece.
Because the phase diagram and the three-dimensional point cloud diagram are aligned on the pixels, corresponding point cloud data can be found from the point clouds of the current scene according to the coordinates of all the pixel points in a certain phase partition, and the point cloud data are the point cloud sheets corresponding to the phase partition. In this way, the slicing processing of the point cloud data is realized through the phase partitions, and the three-dimensional point cloud of the surface of the object to be measured is divided into three-dimensional subspaces corresponding to the phase partitions.
In some embodiments of the present disclosure, step S104 of projecting the three-dimensional point clouds in the respective three-dimensional subspaces to the noise point determination plane area to obtain two-dimensional projections of the point clouds of the respective three-dimensional subspaces in the noise point determination plane area includes:
the noise point determination plane area is configured as a noise point determination plane area having a plurality of sub-determination areas that cover the entire noise point determination plane area.
In a preferred embodiment of the present disclosure, the configuration of the noise point determination plane area includes:
The sizes of the respective sub determination areas in the first direction (for example, the Z direction described below) are configured to be the same; the size of each sub-determination region in a second direction (for example, Y direction described below) perpendicular to the first direction is configured to be positively correlated with the working distance of a camera for acquiring a three-dimensional point cloud of the surface of the object under test.
The first direction is the depth direction of the three-dimensional point cloud, and the first direction, the second direction and the phase increasing direction are mutually perpendicular.
Illustratively, taking the YZ plane of the three-dimensional rectangular coordinate system XYZ as an example of the noise point determination plane area described below, configuring the noise point determination plane area may include:
and segmenting in the Z-axis direction according to the Z-axis coordinate maximum value and the Z-axis coordinate minimum value of the two-dimensional projection to determine the segmentation number and the segmentation coordinates of the Z axis.
Specifically, after the outlier of the point cloud sheet in the Z-axis direction is filtered, a Z-axis coordinate maximum value and a Z-axis coordinate minimum value of the two-dimensional projection of the point cloud sheet, that is, a Z-axis coordinate maximum value and a Z-axis coordinate minimum value of the point cloud sheet, may be determined, and the segmentation may be performed according to a preset Z-axis segmentation interval length.
In some embodiments of the present disclosure, the Z-axis segmentation may be performed in an equally spaced manner, that is, the preset Z-axis interval length may be a fixed value.
In this embodiment, the number of sub-determination regions in the Z-axis direction may be the pair "(Z Max -Z Min ) Z is rounded up to a value Z Max The Z-axis coordinate maximum value of the two-dimensional projection of the point cloud sheet, namely, the Z-axis coordinate maximum value of the point cloud sheet; z is Z min The minimum value of the Z-axis coordinate of the two-dimensional projection of the point cloud sheet, namely the minimum value of the Z-axis coordinate of the point cloud sheet; z is a preset Z-axis segment interval length, that is, the length of the sub-determination area in the Z-axis.
Fig. 3 is a schematic diagram of a dimensional configuration of a sub-determination region in the Z-axis according to an embodiment of the present disclosure.
Referring to FIG. 3, the Z-axis section length, i.e., the size of the sub-determination regions is set to 10mm, and the boundary coordinates of each sub-determination region are Z Min 、Z Min +10、Z Min +20、Z Min +30、……、Z Max 。
As described above in the present disclosure, the present disclosure preferably configures the size of each sub-determination region in a second direction (for example, a Y direction described below) perpendicular to the first direction to be positively correlated with the working distance of a camera for acquiring a three-dimensional point cloud of the surface of the object under test.
Since the resolution of the 3D camera varies with working distance, Y-axis segmentation is performed in a non-equally spaced manner. Specifically, the current resolution of the 3D camera may be determined based on the current working distance of the 3D camera, and then the size of each sub-determination area in the Y-axis direction may be determined based on the current resolution of the 3D camera.
In some embodiments of the present disclosure, the resolution at the current working distance d2 may be determined according to the following equation (1):
wherein D1 represents the measured working distance of the 3D camera, y1 represents the measured resolution of the 3D camera at the working distance D1, y2 represents the current resolution of the 3D camera, and D2 represents the current working distance of the 3D camera. For example, as is known from actual measurement results using a 3D camera, when the 3D camera has a working distance of 1000mm and a resolution in the Y direction is 0.3mm, the resolution of the 3D camera in the Y direction at the current working distance D2 is y2=d2×0.3/1000.
According to the segmentation situation of the Z axis, the current working distance D2 of the 3D camera has a small-to-large change process, so that the resolution Y2 of the 3D camera in the Y direction is different at different working distances, and the size of the sub-determination area in the Y axis direction can be set as the product of the working distance and the resolution Y2 of the 3D camera in the Y direction.
Fig. 4 is an example diagram of configuring a sub determination area on the YZ plane in one embodiment of the present disclosure.
Since the working distances corresponding to the different depths in the Z direction are different, the number of sub-determination regions in the Y direction is different at the different depths in the Z direction.
The number of the partitionable sections in the corresponding Y direction, that is, the number of the sub-determination regions, is different in the Z direction at different depths, the largest partitionable section number is found, for example, the largest partitionable section number is set to be the width projected onto the two-dimensional planar image (that is, the width of the noise point determination planar region described above), and then in the Z depth direction of each layer, it is determined to which sub-determination region the Y value of each projection point belongs, respectively.
In FIG. 4, the vertical axis is the Z axis, the horizontal axis is the Y axis, the Z axis section length is 10mm, and the minimum Z coordinate value of the point cloud is the Z axis Min Has a value of 10, and a maximum value of Z in Z-axis coordinates of Z Max For 90, the coordinates of the segments of the Z axis with the number of (90-10)/10= 9,Z are 10, 20, 30, 40, 50, 60, 70, 80, 90, the number of segments of the Y axis is 5, and the coordinates of the segments of the Y axis are respectively: A. b, C, D, E, F each square in fig. 4 represents a sub-determination area. It should be noted thatThe dimensions of the sub-determination regions in fig. 4 do not represent actual dimensions, and the coordinates of the Y-axis segments do not represent actual coordinates, by way of example only, and as described above, the present disclosure preferably configures the dimensions of the respective sub-determination regions in a second direction (Y-direction) perpendicular to the first direction to be positively correlated with the working distance of a camera for acquiring a three-dimensional point cloud of the surface of the object under test. For example, the coordinates A, B, C, D, E, F of the Y-axis segments can be 0, 2, 5, 9, 14, 20. Those skilled in the art, in light of the technical solution of the present disclosure, adjust the configuration method of the noise point determination plane area, all fall within the protection scope of the present disclosure.
In some embodiments of the present disclosure, points in a point cloud sheet are projected to a noise point determination plane area, coordinates of projection points of points in the point cloud sheet on the noise point determination plane area are determined, and projection points of all points in the point cloud sheet on the noise point determination plane area form two-dimensional projection of the point cloud sheet, wherein coordinates of each pixel point in the two-dimensional projection are coordinates of projection points of points in the point cloud sheet on the noise point determination plane area.
For example, the coordinates of each point in the point cloud sheet can be represented by coordinates in a three-dimensional rectangular coordinate system XYZ, in which the X-axis forward direction is the phase increasing direction, the YZ plane is perpendicular to the phase increasing direction, the Z-axis is parallel to the optical axis of the 3D camera, and the Z-axis forward points to the 3D camera. The noise point judging plane area is configured on the YZ plane, and the coordinates of the projection points of all points in the point cloud sheet on the YZ plane are the Y-axis coordinates and the Z-axis coordinates of the points.
According to a preferred embodiment of the present disclosure, the distribution characteristics of the point cloud of each three-dimensional subspace in the noise point determination plane region described above include the projection point number characteristics in each sub-determination region.
Fig. 5 is a flowchart of a noise point determination method in the three-dimensional point cloud filtering method S100 according to a preferred embodiment of the present disclosure.
Referring to fig. 5, preferably, in S106, determining noise points based on the distribution characteristics of the point clouds of the respective three-dimensional subspaces in the noise point determination plane region includes:
s1062, respectively acquiring the number of projection points of the point cloud of each three-dimensional subspace in each sub-judgment area;
s1064, constructing at least one communication area of the sub-judgment areas based on the number of projection points of the point cloud of each three-dimensional subspace in each sub-judgment area of the noise point judgment plane area;
s1066, determining the point cloud corresponding to the projection point in the sub-determination area outside the communication area as a noise point.
Fig. 6 is a flowchart of a noise point determination method in a three-dimensional point cloud filtering method S100 according to another preferred embodiment of the present disclosure.
Referring to fig. 6, in some embodiments of the present disclosure, in step S1066, further includes: and judging the point cloud corresponding to the projection points in the sub-judgment areas with the number of the projection points in the communication area being smaller than or equal to the threshold value as noise points.
Preferably, step S1064 constructs at least one connected region of the sub-determination regions based on the number of projection points of the point cloud of each three-dimensional subspace within each sub-determination region of the noise point determination plane region, including:
If a certain sub-judgment area has a projection point, and at least one adjacent sub-judgment area with the projection point exists in the sub-judgment area, the sub-judgment area is placed in the communication area.
Fig. 7 shows a count diagram of projection points in the respective sub-determination regions shown in fig. 4.
Referring to fig. 7, each square represents a partition, i.e., a sub-determination area, and the number in each square represents the number of sub-determination areas where the two-dimensional projection points of the dot cloud sheet fall.
And constructing and calculating the connected areas in the obtained two-dimensional counting graph, wherein the threshold value number can be engineering experience values, the working distances of cameras are different, and the threshold value number can be different.
Because the noise point judging plane area used in the three-dimensional point cloud filtering method is provided with a plurality of sub-judging areas, the boundary of the measured object can be divided into different sub-judging areas, and the point cloud filtering with a small number of projection points but belonging to the effective object boundary can be avoided by constructing at least one communication area of the sub-judging areas, so that the accuracy of the point cloud filtering is improved.
Based on the three-dimensional point cloud filtering method provided by the disclosure, the disclosure also provides a three-dimensional reconstruction method based on the three-dimensional point cloud.
Fig. 8 is a flow diagram of a three-dimensional reconstruction method based on a three-dimensional point cloud according to one embodiment of the present disclosure.
Referring to fig. 8, a three-dimensional reconstruction method S200 based on a three-dimensional point cloud of the present disclosure includes:
s202, obtaining an overall phase diagram of the surface of the measured object based on a projection diagram projected onto the surface of the measured object;
s204, acquiring a three-dimensional point cloud representing the surface morphology features of the measured object based on the integral phase diagram;
s206, performing filtering processing on the three-dimensional point cloud to remove noise points in the three-dimensional point cloud;
s208, performing three-dimensional reconstruction of the surface topography features of the measured object based on the three-dimensional point cloud after the noise points are removed.
Wherein filtering processing is performed on the three-dimensional point cloud to remove noise points in the three-dimensional point cloud is implemented based on the three-dimensional point cloud filtering method S100 of any one embodiment of the present disclosure.
Fig. 9 is a block schematic diagram of a three-dimensional point cloud filtering apparatus employing a hardware implementation of a processing system according to one embodiment of the present disclosure.
Fig. 10 is a schematic block diagram of a three-dimensional reconstruction apparatus based on a three-dimensional point cloud employing a hardware implementation of a processing system according to one embodiment of the present disclosure.
The three-dimensional point cloud filtering device and the three-dimensional reconstruction device based on the three-dimensional point cloud can comprise corresponding modules for executing each or several steps in the flow chart. Thus, each step or several steps in the flowcharts described above may be performed by respective modules, and the apparatus may include one or more of these modules. A module may be one or more hardware modules specifically configured to perform the respective steps, or be implemented by a processor configured to perform the respective steps, or be stored within a computer-readable medium for implementation by a processor, or be implemented by some combination.
The hardware architecture may be implemented using a bus architecture. The bus architecture may include any number of interconnecting buses and bridges depending on the specific application of the hardware and the overall design constraints. The buses 1100/2100 connect together various circuits, including one or more processors 1200/2200, memories 1300/2300, and/or hardware modules. The bus 1100/2100 may also connect various other circuits 1400/2400 such as peripherals, voltage regulators, power management circuits, external antennas, and the like.
The bus 1100/2100 may be an industry standard architecture (ISA, industry Standard Architecture) bus, a peripheral component interconnect (PCI, peripheral Component) bus, or an extended industry standard architecture (EISA, extended Industry Standard Component) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one connection line is shown in the figure, but not only one bus or one type of bus.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure. The processor performs the various methods and processes described above. For example, method embodiments in the present disclosure may be implemented as a software program tangibly embodied on a machine-readable medium, such as a memory. In some embodiments, part or all of the software program may be loaded and/or installed via memory and/or a communication interface. One or more of the steps of the methods described above may be performed when a software program is loaded into memory and executed by a processor. Alternatively, in other embodiments, the processor may be configured to perform one of the methods described above in any other suitable manner (e.g., by means of firmware).
Logic and/or steps represented in the flowcharts or otherwise described herein may be embodied in any readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
For the purposes of this description, a "readable storage medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). In addition, the readable storage medium may even be paper or other suitable medium on which the program can be printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner if necessary, and then stored in a memory.
It should be understood that portions of the present disclosure may be implemented in hardware, software, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or part of the steps implementing the method of the above embodiments may be implemented by a program to instruct related hardware, and the program may be stored in a readable storage medium, where the program when executed includes one or a combination of the steps of the method embodiments.
Furthermore, each functional unit in each embodiment of the present disclosure may be integrated into one processing module, or each unit may exist alone physically, or two or more units may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product. The storage medium may be a read-only memory, a magnetic disk or optical disk, etc.
Referring to fig. 9, in some embodiments of the present disclosure, a three-dimensional point cloud filtering apparatus 1000 of the present disclosure includes:
the division module 1002, the division module 1002 divides the overall phase map of the measured object surface obtained based on the projection map projected onto the measured object surface into a plurality of continuous phase partitions to divide the three-dimensional point cloud of the measured object surface obtained based on the overall phase map into respective three-dimensional subspaces corresponding to the respective phase partitions;
the point cloud projection module 1004, the point cloud projection module 1004 projects the three-dimensional point clouds in each three-dimensional subspace to the noise point judgment plane area respectively, so as to obtain the two-dimensional projection of the point clouds in each three-dimensional subspace in the noise point judgment plane area;
the noise determination module 1006 determines noise based on the distribution characteristics of the point clouds of the respective three-dimensional subspaces in the noise determination plane region, respectively, to remove the noise.
Referring to fig. 10, in some embodiments of the present disclosure, a three-dimensional reconstruction apparatus 2000 based on a three-dimensional point cloud of the present disclosure includes:
the overall phase diagram acquisition module 2002, the overall phase diagram acquisition module 2002 acquires an overall phase diagram of the surface of the measured object based on the projection diagram projected onto the surface of the measured object;
The three-dimensional point cloud acquisition module 2004, the three-dimensional point cloud acquisition module 2004 acquires a three-dimensional point cloud representing the surface topography of the measured object based on the whole phase diagram;
the three-dimensional point cloud filtering module 2006, the three-dimensional point cloud filtering module 2006 performs filtering processing on the three-dimensional point cloud to remove noise points in the three-dimensional point cloud;
the three-dimensional reconstruction module 2008, the three-dimensional reconstruction module 2008 performs three-dimensional reconstruction of the surface topography feature of the measured object based on the three-dimensional point cloud after removing the noise points.
The three-dimensional point cloud filtering module 2006 adopts the three-dimensional point cloud filtering device 1000 of the present disclosure.
The present disclosure also provides an electronic device, comprising: a memory storing execution instructions; and a processor executing the execution instructions stored in the memory, causing the processor to execute the three-dimensional point cloud filtering method S100 of any of the embodiments of the present disclosure and/or to execute the three-dimensional reconstruction method S200 of any of the embodiments of the present disclosure.
The present disclosure also provides a readable storage medium having stored therein execution instructions which, when executed by a processor, are to implement the three-dimensional point cloud filtering method S100 of any of the embodiments of the present disclosure and/or implement the three-dimensional reconstruction method S200 of any of the embodiments of the present disclosure.
In the description of the present specification, reference to the terms "one embodiment/mode," "some embodiments/modes," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment/mode or example is included in at least one embodiment/mode or example of the present application. In this specification, the schematic representations of the above terms are not necessarily the same embodiments/modes or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments/modes or examples. Furthermore, the various embodiments/implementations or examples described in this specification and the features of the various embodiments/implementations or examples may be combined and combined by persons skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
It will be appreciated by those skilled in the art that the above-described embodiments are merely for clarity of illustration of the disclosure, and are not intended to limit the scope of the disclosure. Other variations or modifications will be apparent to persons skilled in the art from the foregoing disclosure, and such variations or modifications are intended to be within the scope of the present disclosure.
Claims (12)
1. A noise point determination method, comprising:
respectively acquiring the number of projection points of the three-dimensional point cloud of each three-dimensional subspace in each sub-judgment area, wherein each three-dimensional subspace corresponds to a plurality of phase spaces divided by the integral phase map, and the three-dimensional point cloud of the surface of the measured object acquired based on the integral phase map is divided into each three-dimensional subspace;
constructing at least one communication region of sub-judgment regions based on the number of projection points of three-dimensional point clouds of each three-dimensional subspace in each of a plurality of sub-judgment regions of the noise point judgment plane region, wherein the noise point judgment plane region is a plane region perpendicular to the phase increment direction of the plurality of phase partitions along the whole phase map; and
and judging the three-dimensional point cloud corresponding to the projection points in the sub-judgment areas outside the communication area as noise points.
2. The noise point determination method according to claim 1, further comprising:
and respectively projecting the three-dimensional point clouds in each three-dimensional subspace to the noise point judging plane area to obtain two-dimensional projections of the point clouds of each three-dimensional subspace in the noise point judging plane area, and respectively judging noise points based on the distribution characteristics of the point clouds of each three-dimensional subspace in the noise point judging plane area.
3. The noise point determination method according to claim 1, wherein the plurality of sub-determination areas cover the entire noise point determination plane area.
4. The noise point determination method according to claim 3, wherein the configuration of the noise point determination plane area includes:
the sizes of the respective sub-determination areas in the first direction are configured to be the same;
the dimensions of the respective sub-determination areas in a second direction perpendicular to the first direction are configured to be positively correlated with a working distance of a camera for acquiring a three-dimensional point cloud of the surface of the object under test.
5. The noise point determination method according to claim 4, wherein the first direction is a depth direction of a three-dimensional point cloud, and the first direction, the second direction, and the phase increasing direction are perpendicular to each other.
6. The noise point determination method according to claim 2, wherein the distribution characteristics of the point cloud of each three-dimensional subspace in the noise point determination plane region include projection point number characteristics in each sub-determination region.
7. The noise point determination method according to claim 1, wherein if a certain sub-determination area has a projected point and there is at least one neighboring sub-determination area having a projected point, the sub-determination area is placed in a connected area.
8. The noise point determination method according to claim 7, further comprising:
and secondarily judging the point clouds corresponding to the projection points in the sub-judgment areas with the number of the projection points in the communication area being smaller than or equal to the threshold value as noise points.
9. The noise point determination method according to any one of claims 1 to 8, characterized in that the boundary of the object to be measured can be divided into different sub-determination areas.
10. A noise point determination apparatus, comprising:
the acquisition module is used for respectively acquiring the number of projection points of the three-dimensional point cloud of each three-dimensional subspace in each sub-judgment area, each three-dimensional subspace corresponds to a plurality of phase spaces divided by the integral phase diagram, and the three-dimensional point cloud of the surface of the measured object acquired based on the integral phase diagram is divided into each three-dimensional subspace;
The construction module is used for constructing at least one communication area of the sub-judgment areas based on the number of projection points of the three-dimensional point cloud of each three-dimensional subspace in each sub-judgment area of the noise point judgment plane area, wherein the noise point judgment plane area is a plane area perpendicular to the phase increment direction of the plurality of phase partitions along the whole phase diagram; and
and the judging module judges the three-dimensional point cloud corresponding to the projection points in the sub-judging areas outside the communication area as noise points.
11. An electronic device, comprising:
a memory storing execution instructions; and
a processor executing the execution instructions stored in the memory, causing the processor to execute the noise point determination method according to any one of claims 1 to 9.
12. A readable storage medium having stored therein execution instructions which, when executed by a processor, are to implement the noise point determination method of any one of claims 1 to 9.
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