A labeling object coding method and device based on point cloud data, equipment and storage medium

Abstract

The application discloses a kind of based on point cloud data's label object coding method, device, equipment and storage medium. Obtain point cloud data;According to point cloud data, the label object in point cloud data is identified and classified;Extract the information of label object, the information of label object is encoded into digital sequence, and digital sequence is embedded into point cloud data;Label object is positioned in point cloud data;According to the positioning of label object, control and adjustment are carried out to vehicle. By encoding the information of label object into digital sequence, and then embedding it into point cloud data, the automatic processing and control of label object are realized. Compared with prior art, the method of the present application is more efficient and accurate, and can greatly improve the safety and reliability of autonomous vehicle.

Description

Technical Field

[0001] This invention belongs to the field of vehicle information processing, and specifically relates to a method, apparatus, device and storage medium for annotated object encoding based on point cloud data. Background Technology

[0002] Autonomous driving technology is a rapidly developing field in recent years. Its core involves acquiring environmental information about the vehicle's surroundings through various sensors, then processing and analyzing this information using algorithms to ultimately achieve autonomous driving. In autonomous driving technology, the identification and localization of landmarks is a crucial element, as it helps the vehicle perceive its environment more accurately, thus enabling safer driving.

[0003] Currently, the most commonly used method for marker recognition and localization in the field of autonomous driving is vision-based. This method acquires images of the vehicle's surroundings using cameras, and then uses image processing algorithms to recognize and locate markers.

[0004] During operation, the applicant discovered that existing vision-based methods cannot identify and locate all markers around the vehicle, and that the identification and positioning accuracy of this method has certain limitations. Summary of the Invention

[0005] The purpose of this invention is to solve the above-mentioned technical problems and provide a method, apparatus, device and storage medium for annotating objects based on point cloud data.

[0006] To solve the above problems, the present invention is implemented according to the following technical solution:

[0007] According to one aspect of the present invention, a method for encoding annotations based on point cloud data is provided, the method comprising:

[0008] Acquire point cloud data;

[0009] Based on the point cloud data, the labeled objects in the point cloud data are identified and classified;

[0010] Extract the information of the labeled objects, encode the information of the labeled objects into a digital sequence, and embed the digital sequence into the point cloud data;

[0011] The marked objects are located in the point cloud data;

[0012] Based on the location of the marked objects, the vehicle is controlled and adjusted.

[0013] According to another aspect of the present invention, a marker encoding device based on point cloud data is provided, the device comprising:

[0014] The data acquisition module is used to acquire point cloud data;

[0015] The identification and classification module is used to identify and classify the labeled objects in the point cloud data based on the point cloud data.

[0016] The encoding module is used to extract information from the labeled objects, encode the information from the labeled objects into a digital sequence, and embed the digital sequence into the point cloud data;

[0017] A positioning module is used to locate the marked objects in the point cloud data;

[0018] The control module is used to control and adjust the vehicle based on the positioning of the marker.

[0019] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0020] At least one processor; and

[0021] A memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the point cloud data-based annotation encoding method according to the embodiments of the present invention.

[0023] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program configured to cause a processor to execute and implement the annotation encoding method based on point cloud data as described in the embodiments of the present invention.

[0024] In this embodiment of the invention, point cloud data is acquired; based on the point cloud data, markers in the point cloud data are identified and classified; information of the markers is extracted, encoded into a digital sequence, and embedded into the point cloud data; the markers are located in the point cloud data; and the vehicle is controlled and adjusted based on the location of the markers. By encoding the information of the markers into a digital sequence and then embedding it into the point cloud data, automated processing and control of the markers are achieved. Compared with the prior art, the method of this invention is more efficient and accurate, and can greatly improve the safety and reliability of autonomous vehicles.

[0025] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0026] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, wherein:

[0027] Figure 1 This is a flowchart of a point cloud data-based annotation encoding method according to an embodiment of the present invention;

[0028] Figure 2 This is an example diagram of a point cloud data-based annotation encoding method according to an embodiment of the present invention;

[0029] Figure 3 This is a schematic diagram of the structure of a point cloud data-based annotation encoding device according to an embodiment of the present invention;

[0030] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0033] During its operation, the applicant discovered that real-time identification of surrounding environmental information is currently one of the key technologies in the field of autonomous driving. Existing methods involve labeling objects, training AI models to obtain parameters, and then using these models to detect surrounding objects. This method is computationally intensive, has high latency, and is also costly.

[0034] Furthermore, the most commonly used marker recognition and localization method in the field of autonomous driving is vision-based. This method acquires images of the vehicle's surroundings using cameras, and then uses image processing algorithms to identify and locate markers. While this method has achieved some success, it also has some drawbacks. First, due to the limited field of view of cameras, it cannot identify and locate all markers around the vehicle. Second, due to the complexity of image processing algorithms, the recognition and localization accuracy of this method is also limited.

[0035] Therefore, the purpose of this invention is to provide a vision-based method for marker recognition and localization, which is the most commonly used method in the driving field, compared to vision-based methods. This method acquires images of the vehicle's surroundings using a camera, and then uses image processing algorithms to identify and locate markers. While this method has achieved some success, it also has some drawbacks. First, due to the limited field of view of the camera, it cannot identify and locate all markers around the vehicle. Second, due to the complexity of the image processing algorithms, the recognition and localization accuracy of this method is also limited.

[0036] Figure 1 This is a flowchart of a point cloud data-based annotation encoding method according to an embodiment of the present invention. This embodiment is applicable to the precise localization of annotations in point cloud data surrounding autonomous vehicles. The method can be executed by a point cloud data-based annotation encoding device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0037] Step 101: Obtain point cloud data.

[0038] During operation, autonomous vehicles first scan the surrounding objects to obtain corresponding point cloud data.

[0039] Point cloud data refers to a set of vectors in a three-dimensional coordinate system. Scanned data is recorded in the form of points, each containing three-dimensional coordinates, and some may contain color information (RGB) or reflectance intensity information.

[0040] In this embodiment of the invention, point cloud data around the vehicle is acquired using acquisition devices such as lidar and converted into digitized point cloud data. Specifically, the point cloud data can be a single frame point cloud image or a series of consecutive frame point cloud images.

[0041] Understandably, in order to facilitate the acquisition of point cloud data for the required scene (such as urban roads), the acquisition device can be installed on the top of a mobile device (such as a car) and continuously acquire point cloud data as the mobile device moves.

[0042] Step 102: Identify and classify the labeled objects in the point cloud data based on the point cloud data.

[0043] After acquiring point cloud data, machine learning algorithms are used to process and analyze the point cloud data, that is, to process and analyze the point cloud image and identify and classify the labeled objects in the point cloud data.

[0044] In this invention, the labeled object refers to the object marked in the point cloud image, which can be a car, a person, an obstacle, etc.

[0045] Machine learning algorithms are the core of artificial intelligence and the fundamental way to make computers intelligent. Their applications span all areas of artificial intelligence, and they mainly use induction and synthesis rather than deduction.

[0046] Currently, the technologies involved in point cloud data processing mainly include the following ten: point cloud filtering (data preprocessing), point cloud key points, features and feature description, point cloud registration, point cloud segmentation and classification, SLAM graph optimization, target recognition and retrieval, change retrieval, 3D reconstruction, and point cloud data management.

[0047] Specifically, the processing and analysis of point cloud data, also known as point cloud data annotation, is illustrated below with several steps:

[0048] I. Data Preprocessing

[0049] Before labeling point cloud data, preprocessing is required, such as noise removal, image size and quality adjustment. Image processing tools like OpenCV and ImageMagick can be used to process the point cloud data.

[0050] II. Feature Extraction

[0051] After preprocessing, feature extraction is required from the point cloud data, such as using deep learning algorithms like CNNs and RNNs to extract feature vectors. Feature vectors are an important component of point cloud data and can be used to describe the geometric features and physical properties of the point cloud.

[0052] III. Annotation Framework

[0053] An annotation framework refers to the software tools or platforms used to annotate point cloud data. Common annotation frameworks include Open3D and VTuber. Different annotation frameworks have different annotation methods and formats, and the appropriate annotation framework needs to be selected according to the actual situation.

[0054] IV. Labeled Data

[0055] Use a labeling framework to label point cloud data, including adding labels for points, lines, and circles. The key is to select the appropriate labeling method and format based on the specific situation to ensure the accuracy and usability of the labeling results.

[0056] V. Quality Inspection

[0057] During the annotation process, it is necessary to perform quality checks on the annotated data, such as checking whether the positions of the annotation points are accurate and whether the annotated lines are smooth. Appropriate annotation standards should be selected based on the actual situation to ensure the accuracy and usability of the annotation results.

[0058] VI. Visualization

[0059] After annotation is completed, the annotated data needs to be visualized to view the annotation results. Common visualization tools include VTuber and VTuber3D. The appropriate visualization method and format should be selected based on the specific situation to display the characteristics and attributes of the point cloud data.

[0060] Step 103: Extract the information of the labeled objects, encode the information of the labeled objects into a digital sequence, and embed the digital sequence into the point cloud data.

[0061] The annotations in the point cloud data are identified and classified using machine learning algorithms. At the same time, the information of the annotations is extracted and encoded according to the encoding scheme provided by this invention.

[0062] The information of the markers includes at least color information and location information.

[0063] In the above point cloud data annotation steps, feature extraction includes extracting information of the annotation objects, encoding the annotation objects before annotating the data, and embedding the encoded digital sequence into the point cloud data.

[0064] Encoding the labeled object means converting the information of the labeled object into binary code.

[0065] Specifically, the steps for converting the information of the labeled object into binary code are as follows:

[0066] 1. Scale the point cloud data to a 1:1 scale, that is, scale it proportionally to a size of 1 = 1 meter.

[0067] 2. Arrange the points in the point cloud data in ascending order, with each point including the numbers in the X, Y, and Z directions.

[0068] Point cloud data contains points P1, P2, P3, ..., Pn, where P represents a point and contains three numbers X, Y, and Z, arranged in ascending order as X1, X2, X3, ..., Xn, Y1, Y2, Y3, ..., Yn, and Z1, Z2, Z3, ..., Zn.

[0069] 3. Calculate the average values ​​Mx, My, and Mz in the X, Y, and Z directions, as well as the variances Vx, Vy, and Vz.

[0070] Calculate the average values ​​Mx, My, and Mz in the X, Y, and Z directions, and the variances Vx, Vy, and Vz, respectively.

[0071] 4. The numbers in the X, Y, and Z directions are divided into intervals of 0.1. All points within the interval are calculated according to the formula, and the maximum value is taken. The maximum value is multiplied by 1e10 and then rounded to obtain a 32-bit integer.

[0072] Taking the X direction as an example, starting from X1, each interval is 0.1. All points within that interval are taken and calculated. The formula is:

[0073]

[0074] Given x', take the maximum value among all x' in the interval and multiply it by 1e10, then take the integer part, which will result in a 32-bit integer.

[0075] In addition, if there are no values ​​in the range, then 0 is used.

[0076] 5. A string of byte codes is obtained through calculation in the X, Y, and Z directions. The byte codes are the feature codes of the marked object.

[0077] The values ​​in the X, Y, and Z directions are calculated using the above formula, and a string of bytes is obtained, which is the feature code of the marked object.

[0078] For example, such as Figure 2 As shown, the encoding process for point cloud data includes: data scaling, data arrangement, data splitting, data processing, and data encoding.

[0079] The above encoding scheme is used to encode the information of the labeled objects, and the resulting digital sequence is embedded into the point cloud data.

[0080] Step 104: Locate the labeled objects in the point cloud data.

[0081] By processing and analyzing point cloud data, points in the point cloud data that contain embedded information about labeled objects are located.

[0082] When an autonomous vehicle decides to move forward, it uses the points marked in the location cloud data to determine the correct driving route.

[0083] In practical applications, the localization of labeled objects in point cloud data by autonomous vehicles means that the vehicle's position and orientation can be found on a map. This map is also acquired using only LiDAR, which uses laser beams to measure distances and generate point cloud data, where each point represents the (XYZ) coordinates of an object's surface as acquired by the sensor.

[0084] Step 105: Control and adjust the vehicle according to the location of the marker.

[0085] This invention utilizes vehicle control technology to control and adjust vehicles, thereby achieving automated processing and control of marked objects.

[0086] Among these, localization is the method that enables autonomous vehicles to find their exact location.

[0087] Autonomous vehicles use vehicle control technology to guide them along the correct driving route and ultimately complete their journey.

[0088] Figure 3 This is a schematic diagram of the structure of a point cloud data-based annotation encoding device according to an embodiment of the present invention. Figure 3 As shown, the device includes:

[0089] Data acquisition module 301 is used to acquire point cloud data;

[0090] The identification and classification module 302 is used to identify and classify the labeled objects in the point cloud data based on the point cloud data.

[0091] Encoding module 303 is used to extract information from the labeled objects, encode the information from the labeled objects into a digital sequence, and embed the digital sequence into the point cloud data;

[0092] The positioning module 304 is used to locate the marked object in the point cloud data;

[0093] The control module 305 is used to control and adjust the vehicle according to the positioning of the marker.

[0094] Figure 4 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0095] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0096] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0097] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as annotation encoding methods based on point cloud data.

[0098] In some embodiments, the point cloud-based annotation encoding method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the point cloud-based annotation encoding method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the point cloud-based annotation encoding method by any other suitable means (e.g., by means of firmware).

[0099] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0101] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0102] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0103] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0104] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0105] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the point cloud data-based annotation encoding method provided in this invention.

[0106] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0107] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0108] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for encoding labeled objects based on point cloud data, characterized in that, The method includes: Acquire point cloud data; Based on the point cloud data, the labeled objects in the point cloud data are identified and classified; Extract the information of the labeled objects, encode the information of the labeled objects into a digital sequence, and embed the digital sequence into the point cloud data; The marked objects are located in the point cloud data; Based on the location of the marked objects, the vehicle is controlled and adjusted; Encoding the information of the marker into a digital sequence includes converting the information of the marker into binary code, specifically through the following steps: The point cloud data is scaled proportionally at a 1:1 scale. Arrange the points in the point cloud data in ascending order, and the points include numbers in the X, Y, and Z directions; Calculate the average values ​​Mx, My, and Mz of the numbers in the X, Y, and Z directions, as well as the variances Vx, Vy, and Vz; The numbers in the X, Y, and Z directions are divided into intervals of 0.1, and all points within each interval are calculated according to the formula. After calculation, the maximum value is taken, multiplied by 1e10, and then rounded to obtain a 32-bit integer; The X, Y, and Z directions are calculated to obtain a string of byte codes, which are the feature codes of the marked object.

2. The annotation encoding method based on point cloud data according to claim 1, characterized in that, The identification and classification of the labeled objects in the point cloud data specifically includes: The point cloud data is processed and analyzed using machine learning algorithms to identify and classify the labeled objects in the point cloud data.

3. The annotation encoding method based on point cloud data according to claim 1, characterized in that, Encoding the information of the labeled object into a numerical sequence includes: The information of the labeled object is converted into binary code.

4. The annotation encoding method based on point cloud data according to claim 1, characterized in that, Locating the marked objects in the point cloud data includes: By processing and analyzing the point cloud data, points in the point cloud data that contain information about the labeled objects are located.

5. A labeling and encoding device based on point cloud data, characterized in that, The device includes: The data acquisition module is used to acquire point cloud data; The identification and classification module is used to identify and classify the labeled objects in the point cloud data based on the point cloud data. The encoding module is used to extract information from the labeled objects, encode the information from the labeled objects into a digital sequence, and embed the digital sequence into the point cloud data; A positioning module is used to locate the marked objects in the point cloud data; The control module is used to control and adjust the vehicle based on the positioning of the markers; Encoding the information of the marker into a digital sequence includes converting the information of the marker into binary code, specifically through the following steps: The point cloud data is scaled proportionally at a 1:1 scale. Arrange the points in the point cloud data in ascending order, and the points include numbers in the X, Y, and Z directions; Calculate the average values ​​Mx, My, and Mz of the numbers in the X, Y, and Z directions, as well as the variances Vx, Vy, and Vz; The numbers in the X, Y, and Z directions are divided into intervals of 0.1, and all points within each interval are calculated according to the formula. After calculation, the maximum value is taken, multiplied by 1e10, and then rounded to obtain a 32-bit integer; The X, Y, and Z directions are calculated to obtain a string of byte codes, which are the feature codes of the marked object.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the annotation encoding method based on point cloud data as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that enables a processor to implement the annotation encoding method based on point cloud data as described in any one of claims 1-4 when executed.

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