FEATURE EXTRACTION USING A POINT OF A COLLECTION OF POINTS

An example method for feature extraction includes receiving a selection of a point from a plurality of points, the plurality of points representing an object. The method further includes identifying a feature of interest for the object based at least in part on the point. The method further includes performing edge extraction on the feature of interest. The method further includes performing pre-processing on results of the edge extraction. The method further includes classifying the feature of interest based at least in part on results of the pre-processing. The method further includes constructing, based at least in part on results of the classifying, a geometric primitive or mathematical function that has a best fit to a set of points from the plurality of points associated with the feature of interest. The method further includes generating a graphical representation of the feature of interest using the geometric primitive or mathematical function.

BACKGROUND

The subject matter disclosed herein relates to use of a three-dimensional (3D) laser scanner time-of-flight (TOF) coordinate measurement device. A coordinate measurement device of this type steers a beam of light to a non-cooperative target such as a diffusely scattering surface of an object. A distance meter in the device measures a distance to the object, and angular encoders measure the angles of rotation of two axles in the device. The measured distance and two angles enable a processor in the device to determine the 3D coordinates of the target.

A laser scanner TOF coordinate measurement device (or simply “laser scanner”) is a scanner in which the distance to a target point is determined based on the speed of light in air between the scanner and a target point. Laser scanners are typically used for scanning closed or open spaces such as interior areas of buildings, industrial installations and tunnels. They may also be used, for example, in industrial applications and accident reconstruction applications. A laser scanner optically scans and measures objects in a volume around the scanner through the acquisition of data points representing object surfaces within the volume. Such data points are obtained by transmitting a beam of light onto the objects and collecting the reflected or scattered light to determine the distance, two-angles (i.e., an azimuth and a zenith angle), and optionally a gray-scale value. This raw scan data is collected, stored and sent to a processor or processors to generate a 3D image representing the scanned area or object.

Generating an image requires at least three values for each data point. These three values may include the distance and two angles, or may be transformed values, such as the x, y, z coordinates. In an embodiment, an image is also based on a fourth gray-scale value, which is a value related to irradiance of scattered light returning to the scanner.

Most laser scanner TOF coordinate measurement devices direct the beam of light within the measurement volume by steering the light with a beam steering mechanism. The beam steering mechanism includes a first motor that steers the beam of light about a first axis by a first angle that is measured by a first angular encoder (or another angle transducer). The beam steering mechanism also includes a second motor that steers the beam of light about a second axis by a second angle that is measured by a second angular encoder (or another angle transducer).

Many contemporary laser scanners include a camera mounted on the laser scanner for gathering camera digital images of the environment and for presenting the camera digital images to an operator of the laser scanner. By viewing the camera images, the operator of the scanner can determine the field of view of the measured volume and adjust settings on the laser scanner to measure over a larger or smaller region of space. In addition, the camera digital images may be transmitted to a processor to add color to the scanner image. To generate a color scanner image, at least three positional coordinates (such as x, y, z) and three color values (such as red, green, blue “RGB”) are collected for each data point.

One application where coordinate measurement devices such as laser scanners are used is to scan an object. Another application where coordinate measurement devices such as laser scanners are used in to scan an environment.

Accordingly, while existing coordinate measurement devices are suitable for their intended purposes, what is needed is a coordinate measurement device having certain features of embodiments of the present invention.

BRIEF DESCRIPTION

In one exemplary embodiment, a method for feature extraction is provided. The method includes receiving a selection of a point from a plurality of points, the plurality of points representing an object. The method further includes identifying a feature of interest for the object based at least in part on the point. The method further includes performing edge extraction on the feature of interest. The method further includes performing pre-processing on results of the edge extraction. The method further includes classifying the feature of interest based at least in part on results of the pre-processing. The method further includes constructing, based at least in part on results of the classifying, a geometric primitive or mathematical function that has a best fit to a set of points from the plurality of points associated with the feature of interest. The method further includes generating a graphical representation of the feature of interest using the geometric primitive or mathematical function.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the plurality of points form a point cloud, wherein the point cloud is based on data captured by a three-dimensional (3D) coordinate measurement device.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the 3D coordinate measurement device is a laser scanner.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that performing the edge extraction is performed using tensor voting.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that performing the edge extraction includes: determining a normal of points from the plurality of points associated with the feature of interest; constructing a matrix using the normal; calculating eigen values for the matrix; identifying sharp edge vertices based on the eigen values; and clustering the sharp edge vertices.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that performing the pre-processing includes performing at least one pre-process selected from a group consisting of performing noise reduction on the results of the edge extraction, performing up-sampling on relative less dense areas of the results of the edge extraction, and performing filtering to remove outliers from the results of the edge extraction.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the classifying is performed using a machine learning model.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include training the machine learning model.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that training the machine learning model includes: generating a two-dimensional (2D) mask of primitives; applying a 2D data augmentation to the 2D mask of primitives to generate augmented images; and for each augmented image: identifying contours and applying a point-level transformation, and adding depth information based on the contours and the point-level transformation.

In another exemplary embodiment, a system for feature extraction is provided. The system includes a three-dimensional (3D) coordinate measurement device to collect a plurality of points representing an object. The system further includes a processing system that includes a memory having computer readable instructions and a processing device for executing the computer readable instructions. The computer readable instructions control the processing device to perform operations. The operations include receiving a selection of a point from the plurality of points. The operations further include identifying a feature of interest for the object based at least in part on the point. The operations further include performing edge extraction on the feature of interest. The operations further include classifying the feature of interest based at least in part on results of the edge extraction.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that the plurality of points form a point cloud.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that the 3D coordinate measurement device is a laser scanner.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that performing the edge extraction is performed using tensor voting.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that performing the edge extraction includes: determining a normal of points from the plurality of points associated with the feature of interest; constructing a matrix using the normal; calculating eigen values for the matrix; identifying sharp edge vertices based on the eigen values; and clustering the sharp edge vertices.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that the operations further include performing pre-processing on results of the edge extraction.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that performing the pre-processing includes performing at least one pre-process selected from a group consisting of performing noise reduction on the results of the edge extraction, performing up-sampling on relative less dense areas of the results of the edge extraction, and performing filtering to remove outliers from the results of the edge extraction.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that the classifying is performed using a machine learning model.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that the operations further include training the machine learning model.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that the operations further comprise extracting, based at least in part on results of the classifying, a set of points from the plurality of points associated with the feature of interest.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that the operations further include generating a graphical representation of the feature of interest based at least in part on classifying the feature of interest.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that the 3D coordinate measurement device is a laser scanner.

In another exemplary embodiment, a method for training a machine learning model to classify a feature of interest of an object is provided. The method includes receiving original point cloud training data. The method further includes generating synthetic point cloud training data. The method further includes training the machine learning model using the original point cloud data and the synthetic point cloud training data, the machine learning model generating an output indicating a class of the feature of interest of the object.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that generating the synthetic point cloud training data includes generating a two-dimensional (2D) mask of primitives.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that generating the synthetic point cloud training data includes applying a 2D data augmentation to the 2D mask of primitives to generate augmented images.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that generating the synthetic point cloud training data includes, for each augmented image, identifying contours and applying a point-level transformation.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the point-level transformation is a randomly selected transformation selected from a group consisting of a dropout transformation and a noise transformation.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that generating the synthetic point cloud training data comprises, for each transformed point set, adding depth information based on a real-world sample analysis.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that applying the 2D data augmentation includes applying a distortion to the 2D mask of primitives to generate the augmented images.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that applying the 2D data augmentation includes applying a scaling to the 2D mask of primitives to generate the augmented images.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that applying the 2D data augmentation includes applying a rotation to the 2D mask of primitives to generate the augmented images.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include performing inference on real-world point cloud data to identify, using the point cloud data, a real-world feature of interest for a real-world object.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the original point cloud training data is collected by a three-dimensional coordinate measurement device.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the original point cloud training data represents a real-world feature of interest of a real-world object.

In another exemplary embodiment, a system for training a machine learning model to classify a feature of interest of an object is provided. The system includes a three-dimensional (3D) coordinate measurement device to collect original point cloud training data and a processing system. The processing system includes a memory having computer readable instructions and a processing device for executing the computer readable instructions. The computer readable instructions control the processing device to perform operations. The operations include generating synthetic point cloud training data. The operations further include training the machine learning model using the original point cloud data and the synthetic point cloud training data, the machine learning model generating an output indicating a class of the feature of interest of the object.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that generating the synthetic point cloud training data includes: generating a two-dimensional (2D) mask of primitives; applying a 2D data augmentation to the 2D mask of primitives to generate augmented images; and for each augmented image: identifying contours and applying a point-level transformation, and adding depth information.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that the point-level transformation is a randomly selected transformation selected from a group consisting of a dropout transformation and a noise transformation.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that generating the synthetic point cloud training data includes, for each augmented image, adding depth information based on a real-world sample analysis.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that applying the 2D data augmentation includes applying a distortion to the 2D mask of primitives to generate the augmented images.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that applying the 2D data augmentation includes applying a scaling to the 2D mask of primitives to generate the augmented images.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the system may include that applying the 2D data augmentation includes applying a rotation to the 2D mask of primitives to generate the augmented images.

In another exemplary embodiment, a method for training a machine learning model to classify a feature of interest is provided. The method includes receiving original point cloud training data. The method further includes generating synthetic point cloud training data by generating a two-dimensional (2D) mask of primitives, applying a 2D data augmentation to the 2D mask of primitives to generate augmented images, and for each augmented image, identifying contours and applying a point-level transformation and adding depth information based on real-world sample analysis. The method further includes training the machine learning model using the original point cloud data and the synthetic point cloud training data, the machine learning model generating an output indicating a most probable class of the feature of interest.

Other embodiments described herein implement features of the above-described method in computer systems and computer program products.

DETAILED DESCRIPTION

One or more embodiments described herein relate to feature extraction. Feature extraction involves the identification of a feature of interest of an object and the extraction of a subset of points, from a larger set of points, that are associated with the feature of interest. Feature extraction is useful for inspecting an object, such as to verify whether the object conforms with a reference (e.g., a ground truth), which may be defined by a computer aided design (CAD) model, building information modeling (BIM) model, and/or the like, including combinations and/or multiples thereof.

Conventional feature extraction involves a user selecting points in which the desired feature is included and then the user selecting which feature is going to be extracted so a correct solver can be used. These two steps are user-supervised steps meaning that the user first performs a selection (e.g., segmentation) of points, such as from a point cloud, for a feature of interest of an object because the user knows which points to select. Next, the user classifies the feature of interest by inputting or selecting a shape type (e.g., class) so a correct fitting algorithm can be initialized and executed on the points from the selection.

As an example of conventional feature extraction, a display presents three-dimensional (3D) data, such as a point cloud, of an object to a user. The 3D data can be collected using a 3D coordinate measurement device, such as a laser scanner, as described herein. The user then chooses a selection tool (e.g., rectangular selection tool, lasso selection tool, polygon selection tool, and/or the like, including combinations and/or multiples thereof) and uses the selection tool to define a region of the 3D data (e.g., point cloud) that includes the feature of interest. The user then classifies the feature of interest by inputting on selecting a shape type (e.g., plane, line, circle, sphere, cylinder, cone, torus, round slot, rectangular slot, ellipse, and/or the like). A suitable feature solver then processes the data contained within the defined region using the shape type to identify the feature of interest and extract points associated with the feature of interest. This process is largely manual and requires accurate selections by the user at each stage. However, if either selection is incorrect, conventional feature extraction fails. For example, if the user defines an incorrect region or selects an incorrect shape type, the feature extraction may extract the wrong points or may not function at all.

One or more embodiments described herein addresses these and other shortcomings of conventional feature extraction. According to one or more embodiments described herein, a user selects a point (e.g., a single point) near a feature of interest. Using this point, one or more embodiments described herein performs edge extraction on the feature of interest, performs pre-processing (e.g., denoising, filtering, and/or the like, including combinations and/or multiples thereof) on results of the edge extraction, and classifies the feature of interest based on result of the pre-processing. According to one or more embodiments described herein, the classification can be performed using artificial intelligence (AI), such as machine learning (ML). This eliminates potential incorrect classification by a user and reduces demands on the user.

One or more embodiments described herein provide one or more advantages over the prior art. For example, one or more embodiments described herein provide for identifying features of interest in point cloud data based on a single point selection where the feature of interest is automatically identified and classified based on the selected point. This process provides more accurate and precise feature identification when processing point cloud data. Further, one or more embodiments described herein provide for generating synthetic point cloud data used to train a machine learning model to classify features of interest. Generating synthetic point cloud data improves training a machine learning model to perform classification where insufficient original training data is available. Further, generating the synthetic point cloud data reduces the amount of resources (e.g., memory, processing load, etc.) associated with collecting (e.g., by performing a scan using the scanner520) original training data. Increased training data improves the resulting trained machine learning model to achieve better generalization capability (e.g., perform better on unseen data). Further, synthetic data provides for training more accurate and efficient machine learning models because the synthetic training data can be created using techniques that manipulate the data (e.g., transformations) that provide additional views that may not be available in original training data. According to an example, transformations used to create the synthetic training data can be tunned based on real world sample statistics (e.g., amount of depth points, amount of noise, and/or the like, including combinations and/or multiples thereof).

Referring now toFIGS.1-3, a 3D coordinate measurement device, such as a laser scanner20, is shown for optically scanning and measuring the environment surrounding the laser scanner20according to one or more embodiments described herein. The laser scanner20has a measuring head22and a base24. The measuring head22is mounted on the base24such that the laser scanner20may be rotated about a vertical axis23. In one embodiment, the measuring head22includes a gimbal point27that is a center of rotation about the vertical axis23and a horizontal axis25. The measuring head22has a rotary mirror26, which may be rotated about the horizontal axis25. The rotation about the vertical axis may be about the center of the base24. The terms vertical axis and horizontal axis refer to the scanner in its normal upright position. It is possible to operate a 3D coordinate measurement device on its side or upside down, and so to avoid confusion, the terms azimuth axis and zenith axis may be substituted for the terms vertical axis and horizontal axis, respectively. The term pan axis or standing axis may also be used as an alternative to vertical axis.

The measuring head22is further provided with an electromagnetic radiation emitter, such as light emitter28, for example, that emits an emitted light beam30. In one embodiment, the emitted light beam30is a coherent light beam such as a laser beam. The laser beam may have a wavelength range of approximately 300 to 1600 nanometers, for example 790 nanometers, 905 nanometers, 1550 nm, or less than 400 nanometers. It should be appreciated that other electromagnetic radiation beams having greater or smaller wavelengths may also be used. The emitted light beam30is amplitude or intensity modulated, for example, with a sinusoidal waveform or with a rectangular waveform. The emitted light beam30is emitted by the light emitter28onto a beam steering unit, such as mirror26, where it is deflected to the environment. A reflected light beam32is reflected from the environment by an object34. The reflected or scattered light is intercepted by the rotary mirror26and directed into a light receiver36. The directions of the emitted light beam30and the reflected light beam32result from the angular positions of the rotary mirror26and the measuring head22about the axes25and23, respectively. These angular positions in turn depend on the corresponding rotary drives or motors.

Coupled to the light emitter28and the light receiver36is a controller38. The controller38determines, for a multitude of measuring points X, a corresponding number of distances d between the laser scanner20and the points X on object34. The distance to a particular point X is determined based at least in part on the speed of light in air through which electromagnetic radiation propagates from the device to the object point X. In one embodiment the phase shift of modulation in light emitted by the laser scanner20and the point X is determined and evaluated to obtain a measured distance d.

The speed of light in air depends on the properties of the air such as the air temperature, barometric pressure, relative humidity, and concentration of carbon dioxide. Such air properties influence the index of refraction n of the air. The speed of light in air is equal to the speed of light in vacuum c divided by the index of refraction. In other words, cair=c/n. A laser scanner of the type discussed herein is based on the time-of-flight (TOF) of the light in the air (the round-trip time for the light to travel from the device to the object and back to the device). Examples of TOF scanners include scanners that measure round trip time using the time interval between emitted and returning pulses (pulsed TOF scanners), scanners that modulate light sinusoidally and measure phase shift of the returning light (phase-based scanners), as well as many other types. A method of measuring distance based on the time-of-flight of light depends on the speed of light in air and is therefore easily distinguished from methods of measuring distance based on triangulation. Triangulation-based methods involve projecting light from a light source along a particular direction and then intercepting the light on a camera pixel along a particular direction. By knowing the distance between the camera and the projector and by matching a projected angle with a received angle, the method of triangulation enables the distance to the object to be determined based on one known length and two known angles of a triangle. The method of triangulation, therefore, does not directly depend on the speed of light in air.

In one mode of operation, the scanning of the volume around the laser scanner20takes place by rotating the rotary mirror26relatively quickly about axis25while rotating the measuring head22relatively slowly about axis23, thereby moving the assembly in a spiral pattern. In an exemplary embodiment, the rotary mirror rotates at a maximum speed of 5820 revolutions per minute. For such a scan, the gimbal point27defines the origin of the local stationary reference system. The base24rests in this local stationary reference system.

In addition to measuring a distance d from the gimbal point27to an object point X, the laser scanner20may also collect gray-scale information related to the received optical power (equivalent to the term “brightness.”) The gray-scale value may be determined at least in part, for example, by integration of the bandpass-filtered and amplified signal in the light receiver36over a measuring period attributed to the object point X.

The measuring head22may include a display device40integrated into the laser scanner20. The display device40may include a graphical touch screen41, as shown inFIG.1, which allows the operator to set the parameters or initiate the operation of the laser scanner20. For example, the screen41may have a user interface that allows the operator to provide measurement instructions to the device, and the screen may also display measurement results.

The laser scanner20includes a carrying structure42that provides a frame for the measuring head22and a platform for attaching the components of the laser scanner20. In one embodiment, the carrying structure42is made from a metal such as aluminum. The carrying structure42includes a traverse member44having a pair of walls46,48on opposing ends. The walls46,48are parallel to each other and extend in a direction opposite the base24. Shells50,52are coupled to the walls46,48and cover the components of the laser scanner20. In the exemplary embodiment, the shells50,52are made from a plastic material, such as polycarbonate or polyethylene for example. The shells50,52cooperate with the walls46,48to form a housing for the laser scanner20.

On an end of the shells50,52opposite the walls46,48a pair of yokes54,56are arranged to partially cover the respective shells50,52. In the exemplary embodiment, the yokes54,56are made from a suitably durable material, such as aluminum for example, that assists in protecting the shells50,52during transport and operation. The yokes54,56each includes a first arm portion58that is coupled, such as with a fastener for example, to the traverse44adjacent the base24. The arm portion58for each yoke54,56extends from the traverse44obliquely to an outer corner of the respective shell50,52. From the outer corner of the shell, the yokes54,56extend along the side edge of the shell to an opposite outer corner of the shell. Each yoke54,56further includes a second arm portion that extends obliquely to the walls46,48. It should be appreciated that the yokes54,56may be coupled to the traverse42, the walls46,48and the shells50,54at multiple locations.

The pair of yokes54,56cooperate to circumscribe a convex space within which the two shells50,52are arranged. In the exemplary embodiment, the yokes54,56cooperate to cover all of the outer edges of the shells50,54, while the top and bottom arm portions project over at least a portion of the top and bottom edges of the shells50,52. This provides advantages in protecting the shells50,52and the measuring head22from damage during transportation and operation. In other embodiments, the yokes54,56may include additional features, such as handles to facilitate the carrying of the laser scanner20or attachment points for accessories for example.

On top of the traverse44, a prism60is provided. The prism extends parallel to the walls46,48. In the exemplary embodiment, the prism60is integrally formed as part of the carrying structure42. In other embodiments, the prism60is a separate component that is coupled to the traverse44. When the mirror26rotates, during each rotation the mirror26directs the emitted light beam30onto the traverse44and the prism60. Due to non-linearities in the electronic components, for example in the light receiver36, the measured distances d may depend on signal strength, which may be measured in optical power entering the scanner or optical power entering optical detectors within the light receiver36, for example. In an embodiment, a distance correction is stored in the scanner as a function (possibly a nonlinear function) of distance to a measured point and optical power (generally unscaled quantity of light power sometimes referred to as “brightness”) returned from the measured point and sent to an optical detector in the light receiver36. Since the prism60is at a known distance from the gimbal point27, the measured optical power level of light reflected by the prism60may be used to correct distance measurements for other measured points, thereby allowing for compensation to correct for the effects of environmental variables such as temperature. In the exemplary embodiment, the resulting correction of distance is performed by the controller38.

In an embodiment, the base24is coupled to a swivel assembly (not shown) such as that described in commonly owned U.S. Pat. No. 8,705,012 ('012), which is incorporated by reference herein. The swivel assembly is housed within the carrying structure42and includes a motor138that is configured to rotate the measuring head22about the axis23. In an embodiment, the angular/rotational position of the measuring head22about the axis23is measured by angular encoder134.

An auxiliary image acquisition device66may be a device that captures and measures a parameter associated with the scanned area or the scanned object and provides a signal representing the measured quantities over an image acquisition area. The auxiliary image acquisition device66may be, but is not limited to, a pyrometer, a thermal imager, an ionizing radiation detector, or a millimeter-wave detector. In an embodiment, the auxiliary image acquisition device66is a color camera.

In an embodiment, a central color camera (first image acquisition device)112is located internally to the scanner and may have the same optical axis as the 3D scanner device. In this embodiment, the first image acquisition device112is integrated into the measuring head22and arranged to acquire images along the same optical pathway as emitted light beam30and reflected light beam32. In this embodiment, the light from the light emitter28reflects off a fixed mirror116and travels to dichroic beam-splitter118that reflects the light117from the light emitter28onto the rotary mirror26. In an embodiment, the mirror26is rotated by a motor136and the angular/rotational position of the mirror is measured by angular encoder134. The dichroic beam-splitter118allows light to pass through at wavelengths different than the wavelength of light117. For example, the light emitter28may be a near infrared laser light (for example, light at wavelengths of 780 nm or 1250 nm), with the dichroic beam-splitter118configured to reflect the infrared laser light while allowing visible light (e.g., wavelengths of 400 to 700 nm) to transmit through. In other embodiments, the determination of whether the light passes through the beam-splitter118or is reflected depends on the polarization of the light. The digital camera112obtains 2D images of the scanned area to capture color data to add to the scanned image. In the case of a built-in color camera having an optical axis coincident with that of the 3D scanning device, the direction of the camera view may be easily obtained by simply adjusting the steering mechanisms of the scanner—for example, by adjusting the azimuth angle about the axis23and by steering the mirror26about the axis25.

Referring now toFIG.4with continuing reference toFIGS.1-3, elements are shown of the laser scanner20. Controller38is a suitable electronic device capable of accepting data and instructions, executing the instructions to process the data, and presenting the results. The controller38includes one or more processing elements122. The processors may be microprocessors, field programmable gate arrays (FPGAs), digital signal processors (DSPs), and generally any device capable of performing computing functions. The one or more processors122have access to memory124for storing information.

Controller38is capable of converting the analog voltage or current level provided by light receiver36into a digital signal to determine a distance from the laser scanner20to an object in the environment. Controller38uses the digital signals that act as input to various processes for controlling the laser scanner20. The digital signals represent one or more laser scanner20data including but not limited to distance to an object, images of the environment, images acquired by panoramic camera126, angular/rotational measurements by a first or azimuth encoder132, and angular/rotational measurements by a second axis or zenith encoder134.

In general, controller38accepts data from encoders132,134, light receiver36, light source28, and panoramic camera126and is given certain instructions for the purpose of generating a 3D point cloud of a scanned environment. Controller38provides operating signals to the light source28, light receiver36, panoramic camera126, zenith motor136and azimuth motor138. The controller38compares the operational parameters to predetermined variances and if the predetermined variance is exceeded, generates a signal that alerts an operator to a condition. The data received by the controller38may be displayed on a user interface40coupled to controller38. The user interface40may be one or more LEDs (light-emitting diodes)82, an LCD (liquid-crystal diode) display, a CRT (cathode ray tube) display, a touch-screen display or the like. A keypad may also be coupled to the user interface for providing data input to controller38. In one embodiment, the user interface is arranged or executed on a mobile computing device that is coupled for communication, such as via a wired or wireless communications medium (e.g. Ethernet, serial, USB, Bluetooth™ or WiFi) for example, to the laser scanner20.

The controller38may also be coupled to external computer networks such as a local area network (LAN) and the Internet. A LAN interconnects one or more remote computers, which are configured to communicate with controller38using a well-known computer communications protocol such as TCP/IP (Transmission Control Protocol/Internet({circumflex over ( )}) Protocol), RS-232, ModBus, and the like. Additional systems20may also be connected to LAN with the controllers38in each of these systems20being configured to send and receive data to and from remote computers and other systems20. The LAN may be connected to the Internet. This connection allows controller38to communicate with one or more remote computers connected to the Internet.

The processors122are coupled to memory124. The memory124may include random access memory (RAM) device140, a non-volatile memory (NVM) device142, and a read-only memory (ROM) device144. In addition, the processors122may be connected to one or more input/output (I/O) controllers146and a communications circuit148. In an embodiment, the communications circuit92provides an interface that allows wireless or wired communication with one or more external devices or networks, such as the LAN discussed above.

Controller38includes operation control methods embodied in application code (e.g., program instructions executable by a processor to cause the processor to perform operations). These methods are embodied in computer instructions written to be executed by processors122, typically in the form of software. The software can be encoded in any language, including, but not limited to, assembly language, VHDL (Verilog Hardware Description Language), VHSIC HDL (Very High Speed IC Hardware Description Language), Fortran (formula translation), C, C++, C#, Objective-C, Visual C++, Java, ALGOL (algorithmic language), BASIC (beginners all-purpose symbolic instruction code), visual BASIC, ActiveX, HTML (HyperText Markup Language), Python, Ruby and any combination or derivative of at least one of the foregoing.

It should be appreciated that while embodiments herein describe the 3D coordinate measurement device as being a laser scanner, this is for example purposes and the claims should not be so limited. In other embodiments, the 3D coordinate measurement device may be another type of system that measures a plurality of points on surfaces (i.e., generates a point cloud), such as but not limited to a triangulation scanner, a structured light scanner, or a photogrammetry device for example.

FIG.5is a schematic illustration of a processing system500for performing feature extraction according to one or more embodiments described herein. The processing system500includes a processing device502(e.g., one or more of the processing devices1121ofFIG.11), a system memory504(e.g., the RAM1124and/or the ROM1122ofFIG.11), a network adapter506(e.g., the network adapter1126ofFIG.11), a data store508, a display510, a camera511, an identification engine512, an edge extraction engine513, a pre-processing engine514, a classification engine515, a fitting engine516, a graphical representation engine517, and a machine learning (ML) training engine518.

The various components, modules, engines, etc. described regardingFIG.5(e.g., the identification engine512, the edge extraction engine513, the pre-processing engine514, the classification engine515, the fitting engine516, the graphical representation engine517, and the ML training engine518) can be implemented as instructions stored on a computer-readable storage medium, as hardware modules, as special-purpose hardware (e.g., application specific hardware, application specific integrated circuits (ASICs), application specific special processors (ASSPs), field programmable gate arrays (FPGAs), as embedded controllers, hardwired circuitry, etc.), or as some combination or combinations of these. According to aspects of the present disclosure, the engine(s) described herein can be a combination of hardware and programming. The programming can be processor executable instructions stored on a tangible memory, and the hardware can include the processing device502for executing those instructions. Thus, the system memory504can store program instructions that when executed by the processing device502implement the engines described herein. Other engines can also be utilized to include other features and functionality described in other examples herein.

The network adapter506enables the processing system500to transmit data to and/or receive data from other sources, such as the scanner520. For example, the processing system500receives data (e.g., a data set that includes a plurality of three-dimensional coordinates of an object522) from the scanner520directly and/or via a network507. The data from the scanner520can be stored in the data store508of the processing system500as data509, which can be used to display a point cloud or other graphical representation on the display510. According to one or more embodiments described herein, the camera511can capture images of the object522, which may be presented on the display510as a video stream of the object522.

The scanner520(e.g., a laser scanner) can be arranged on, in, and/or around the object522to scan the object522. It should be appreciated that while embodiments herein refer to a 3D coordinate measurement device as a laser scanner (e.g., the scanner520), this is for example purposes and the claims should not be so limited. In other embodiments, other types of optical measurement devices may be used, such as but not limited to triangulation scanners and structured light scanners for example.

According to one or more embodiments described herein, the scanner520can include a scanner processing system including a scanner controller, a housing, and a three-dimensional (3D) scanner. The 3D scanner can be disposed within the housing and operably coupled to the scanner processing system. The 3D scanner includes a light source, a beam steering unit, a first angle measuring device, a second angle measuring device, and a light receiver. The beam steering unit cooperates with the light source and the light receiver to define a scan area. The light source and the light receiver are configured to cooperate with the scanner processing system to determine a first distance to a first object point based at least in part on a transmitting of a light by the light source and a receiving of a reflected light by the light receiver. The 3D scanner is further configured to cooperate with the scanner processing system to determine 3D coordinates of the first object point based at least in part on the first distance, a first angle of rotation, and a second angle of rotation.

The scanner520performs at least one scan to generate a data set that includes a plurality of three-dimensional coordinates of the object522. The data set can be transmitted, directly or indirectly (such as via the network507) to a processing system, such as the processing system500, which can store the data set as the data509in the data store508. It should be appreciated that other numbers of scanners (e.g., one scanner, three scanners, four scanners, six scanners, eight scanners, etc.) can be used. According to one or more embodiments described herein, one or more scanners can be used to take multiple scans. For example, the scanner520can capture first scan data of the object522at a first location and then be moved to a second location, where the scanner520captures second scan data of the object522.

Using the data received from the scanner520, the processing system500can perform feature extraction using the data509using one or more of the point the identification engine512, the edge extraction engine513, the pre-processing engine514, the classification engine515, the fitting engine516, the graphical representation engine517, and the ML training engine518. For example, the identification engine512identifies a feature of interest for the object522based on a selected point from a plurality of points captured by the scanner520. The edge extraction engine513performs edge extraction on the feature of interest, and the pre-processing engine514performs pre-processing (e.g., denoising, filtering, and/or the like, including combinations and/or multiples thereof) on results of the edge extraction. The classification engine515classifies the object522based on results of the pre-processing. The classification engine515can implement artificial intelligence, such as machine learning, by implementing a trained machine learning model to perform the classification. The trained machine learning model can be trained by the ML training engine518. The fitting engine516acts as a solver that performs a process of constructing a geometric primitive, or mathematical function, that has the best fit to a series of data points. The output of the solver (e.g., the fitting engine516) is the geometric primitive or mathematical function that is then used to create the graphical representation. The graphical representation engine517generates a graphical representation of the feature of interest. The features and functions of the engines512-518are now described in more detail with reference toFIGS.6and7A-7Eas examples.

Turning now toFIG.6, a flow diagram of a method600for feature extraction using a point of a collection of points according to one or more embodiments described herein. The method600can be performed by any suitable system or device, such as the processing system500ofFIG.5, the machine learning training and inference system800ofFIG.8, and/or the processing system1100ofFIG.11. The method600is now described with further reference toFIGS.5and7A-7E.

Turning now toFIG.6, at block602, a processing system (e.g., the processing system500) receives, such as from a user of the processing system, a selection of a point from a plurality of points. The plurality of points represents an object (e.g., the object522). For example,FIG.7Ais a representation700that represents an object701, which can be any suitable object, portion of an object, and/or the like, including combinations and/or multiples thereof. In this example, the object701includes two features702,703(e.g., openings) shown in the representation700. The object701is scanned, such as by a 3D coordinate measurement device (e.g., the scanner520), to capture points704(e.g., 3D coordinates) that represent the object701and form the representation700. Each of the points can be represented by three-dimensional coordinates (e.g., “x,y,z”). The user can select a point704afrom the points704.

With continued reference toFIG.6, at block604, the processing system identifies (e.g., using the identification engine512) a feature of interest for the object based at least in part on the point. That is, the point is used to identify a feature of interest. With reference toFIG.7A, the feature of interest can be the feature703, which is defined by an edge706. In this example, the feature of interest is the feature703as opposed to the feature702due to the proximity of the point704aselected by the user relative to the feature703. That is, the point704ais closer to the feature703than the point704ais to the feature702. Thus, the feature703is considered to be the feature of interest.

With continued reference toFIG.6, at block606, the processing system performs (e.g., using the edge extraction engine513) edge extraction on the feature of interest. Edge extraction extracts points associated with the feature of interest. For example, inFIG.7A, a subset of the points represents the feature of interest (e.g., the feature703). InFIG.7B, a representation710shows the subset of the points that represent the feature of interest as points711.

According to one or more embodiments described herein, the edge extraction at block606is performed using tensor voting. Tensor voting involves the perceptional grouping or organization of points to extract features. As described herein, each of the points can be represented by three-dimensional coordinates. Additionally, or alternatively, in an embodiment, each of the points can be represented by a tensor, which is a container that stores data in “n” dimensions. One example of tensor voting is described in the publication entitled “Tensor Voting” by Gerard Medioni, which is incorporated by reference in its entirety. In tensor voting, each point communicates its information (in the form of a tensor) to its neighborhood through a tensor field and casts a tensor vote. The votes are collected at each point for votes cast at that point and a new tensor is generated. A matching process can be performed to detect features. By using tensor voting, the processing system identifies the subset of the points that represent the feature of interest (e.g., the points711ofFIG.7B)

According to one or more embodiments described herein, edge extraction at block606is determined by performing a spectral analysis which includes determining the “normal” of the points (for example, shown by the vector712ofFIG.7B) from the feature of interest and constructing a matrix using the normal of the points. Then, the eigen values for the matrix are calculated, and sharp edge vertices are identified. The sharp edge vertices can be identified by calculating the vertices considered to be sharp edges (e.g., defined thresholds for each matrix based on curvature (eigen values)), and the sharp edge vertices are then clustered, resulting in extracted points that represent the edge (e.g., the points711ofFIG.7B).

With continued reference toFIG.6, at block608, the processing system performs (e.g., using the pre-processing engine514) pre-processing on results of the edge extraction. Pre-processing can include performing noise reduction, performing up-sampling on relative less dense areas (e.g., the area713ofFIG.7Bas compared to the area714), performing filtering to remove outliers, and/or the like, including combinations and/or multiples thereof. The representation720ofFIG.7Cincludes points721that represent the feature of interest subsequent to the pre-processing at block608.

With continued reference toFIG.6, at block610, the processing system classifies (e.g., using the classification engine515) the feature of interest based at least in part on results of the pre-processing. For example, the processing system classifies the object701based on one of a plurality of object classes. As shown inFIG.7D, object classes730are shown. In this example, the object classes730include “circle,” “ellipse,” “round slot,” “rectangle,” and “cylinder.” The object classes730are merely examples, and other object classes may be used in other examples.

According to one or more embodiments, the processing system uses a trained machine learning model (e.g., a trained model519) to classify the objects. Artificial intelligence, which includes machine learning is further described herein, including training (e.g., using the ML training engine518) the machine learning model. In the example ofFIGS.7A-7E, the processing system classifies the points721(which represent the feature of interest (e.g., the feature703)) as a “circle.” According to examples, each of the object classes730can include a likelihood or probability that the classification is correct. InFIG.7D, the likelihood or probability (e.g., a score between0and1, where0indicates no probability and1indicates a certainty) for each of the classes is represented by a horizontal scale, where a greater probability is indicated by a scale extended farther towards the right. Thus, in this example, the “circle” has a greatest likelihood or probability as compared to the “ellipse,” “round slot,” “rectangle,” and “cylinder” classes.

According to an example, the ML training engine518, performs machine learning training to train the trained model519. One example of a method for training the trained model519is as follows: generating a two-dimensional (2D) mask of primitives; applying a 2D data augmentation to the 2D mask of primitives to generate augmented images; and for each augmented image: identifying contours and applying a point-level transformation, and adding depth information based on the contours and the point-level transformation. Training the trained model519is further described herein with reference toFIG.8et seq.

With continued reference toFIG.6, at block612, the processing system constructs (e.g., using the fitting engine516), based at least in part on results of the classifying, a geometric primitive or mathematical function that has a best fit to a set of points from the plurality of points associated with the feature of interest. According to one or more embodiments described herein, the fitting engine516uses a set of points and an expected geometric form as inputs to compute a geometric primitive. For example, for circles and ellipses, a least squares fitting approach can be used.FIG.7Cshows an example in which the points721that represent the feature of interest are extracted from the points for the edge706. This representation is then fed into the classifier (e.g., using the classification engine515) which outputs the category “circle” as the most probable class (730) for this example. Based on this classification, the circle best fit engine is then used on the points that represent the feature of interest which results in a circle primitive.

At block614, the processing system generates (e.g., using the graphical representation engine517) a graphical representation740of the feature of interest using the set of points. In the example ofFIG.7E, the feature of interest is represented by the circle741, which represents the fitting output of the edge706of the feature of interest (e.g., the feature703). The size of the hole (e.g., the feature of interest703) can be verified using the circle741, for example, such as by comparing a size of the circle741with a reference circle (e.g., a ground truth) for the feature703. This provides for inspecting and verifying objects, such as the object701.

It should be understood that the process depicted inFIG.6represents an illustration, and that other processes may be added or existing processes may be removed, modified, or rearranged without departing from the scope of the present disclosure.

One or more embodiments described herein can utilize machine learning techniques to perform tasks, such as classifying a feature of interest. More specifically, one or more embodiments described herein can incorporate and utilize rule-based decision making and artificial intelligence (AI) reasoning to accomplish the various operations described herein, namely classifying a feature of interest. The phrase “machine learning” broadly describes a function of electronic systems that learn from data. A machine learning system, engine, or module can include a trainable machine learning algorithm that can be trained, such as in an external cloud environment, to learn functional relationships between inputs and outputs, and the resulting model (sometimes referred to as a “trained neural network,” “trained model,” and/or “trained machine learning model”) can be used for classifying a feature of interest, for example. In one or more embodiments, machine learning functionality can be implemented using an Artificial Neural Network (ANN) having the capability to be trained to perform a function. In machine learning and cognitive science, ANNs are a family of statistical learning models inspired by the biological neural networks of animals, and in particular the brain. ANNs can be used to estimate or approximate systems and functions that depend on a large number of inputs. Convolutional Neural Networks (CNN) are a class of deep, feed-forward ANNs that are particularly useful at tasks such as, but not limited to analyzing visual imagery and natural language processing (NLP). Recurrent Neural Networks (RNN) are another class of deep, feed-forward ANNs and are particularly useful at tasks such as, but not limited to, unsegmented connected handwriting recognition and speech recognition. Other types of neural networks are also known and can be used in accordance with one or more embodiments described herein.

ANNs can be embodied as so-called “neuromorphic” systems of interconnected processor elements that act as simulated “neurons” and exchange “messages” between each other in the form of electronic signals. Similar to the so-called “plasticity” of synaptic neurotransmitter connections that carry messages between biological neurons, the connections in ANNs that carry electronic messages between simulated neurons are provided with numeric weights that correspond to the strength or weakness of a given connection. The weights can be adjusted and tuned based on experience, making ANNs adaptive to inputs and capable of learning. For example, the weights are adjusted via backpropagation that aims to reduce the error (defined by a loss function) between sample ground truth data and a predicted label on each iteration of learning (also known as epoch). As an example, an ANN for handwriting recognition is defined by a set of input neurons that can be activated by the pixels of an input image. After being weighted and transformed by a function determined by the network's designer, the activation of these input neurons are then passed to other downstream neurons, which are often referred to as “hidden” neurons. This process is repeated until an output neuron is activated. The activated output neuron determines which character was input. It should be appreciated that these same techniques can be applied in the case of classifying a feature of interest as described herein (see, e.g.,FIG.6).

Systems for training and using a machine learning model are now described in more detail with reference toFIG.8. Particularly,FIG.8depicts a block diagram of components of a machine learning training and inference system800according to one or more embodiments described herein. The system800performs training802and inference804. During training802, the ML training engine518trains a model (e.g., the trained model519) to perform a task, such as to classifying a feature of interest. Inference804is the process of implementing the trained model519to perform the task, such as to classify a feature of interest, in the context of a larger system (e.g., a system826). All or a portion of the system800shown inFIG.8can be implemented, for example by all or a subset of the processing system500OFFIG.5.

The training802begins with training data812, which may be structured or unstructured data. According to one or more embodiments described herein, the training data812includes original point cloud data and/or synthetic point cloud data. The ML training engine518receives the training data812and a model form814. The model form814represents a base model that is untrained. The model form814can have preset weights and biases, which can be adjusted during training. It should be appreciated that the model form814can be selected from many different model forms depending on the task to be performed. For example, where the training802is to train a model to perform image classification, the model form814may be a model form of a CNN. The training802can be supervised learning, semi-supervised learning, unsupervised learning, reinforcement learning, and/or the like, including combinations and/or multiples thereof. For example, supervised learning can be used to train a machine learning model to classify an object of interest in an image. To do this, the training data812includes labeled images, including images of the object of interest with associated labels (ground truth) and other images that do not include the object of interest with associated labels. In this example, the ML training engine518takes as input a training image from the training data812, makes a prediction for classifying the image, and compares the prediction to the known label. The ML training engine518then adjusts weights and/or biases of the model based on results of the comparison, such as by using backpropagation. The training802may be performed multiple times (referred to as “epochs”) until a suitable model is trained (e.g., the trained model519).

Once trained, the trained model519can be used to perform inference804to perform a task, such as to classify a feature of interest. The inference engine820applies the trained model519to new data822(e.g., real-world, non-training data). For example, if the trained model519is trained to classify images of a particular object, such as a chair, the new data822can be an image of a chair that was not part of the training data812. In this way, the new data822represents data to which the model trained has not been exposed. The inference engine820makes a prediction824(e.g., a classification of an object in an image of the new data822) and passes the prediction824to the system826(e.g., the processing system500ofFIG.5). The system826can, based on the prediction824, taken an action, perform an operation, perform an analysis, and/or the like, including combinations and/or multiples thereof. In some embodiments, the system826can add to and/or modify the new data822based on the prediction824.

In accordance with one or more embodiments, the predictions824generated by the inference engine820are periodically monitored and verified to ensure that the inference engine820is operating as expected. Based on the verification, additional training802may occur using the trained model519as the starting point. The additional training802may include all or a subset of the original training data812and/or new training data812. In accordance with one or more embodiments, the training802includes updating the trained model519to account for changes in expected input data.

Training machine learning models (e.g., the trained model519) uses training data (e.g., the training data812). In some cases, sufficient training data may not be available. Without sufficient training data, models cannot be trained to a desired level of accuracy, for example. For example, according to one or more embodiments described herein, a model trained with insufficient training data may not be able to correctly classify a feature of interest (e.g., a circle (see, e.g.,FIG.7A) may be incorrectly classified as another shape, such as an ellipse or round slot).

In an effort to cure this deficiency (e.g., lack of sufficient training data), one or more embodiments described herein provides for using synthetic training data for training a machine learning model that can be used for classifying features of interest. Synthetic data acts as a substitute for or supplement to real-world training data (referred to as “original” training data) and provides similar properties as the real-world training data. Thus, the synthetic data increases the amount of data available for training machine learning models. There are two primary types of synthetic training data: fully synthetic training data (e.g., no real-world data available) and partially synthetic training data (e.g., some real-world data available, and the synthetic data is aimed to be similar to this real-world data). One or more embodiments described herein generates point cloud primitives (e.g., cylinders, spheres, circles, rectangles, and/or the like, including combinations and/or multiples thereof) based on previously acquired and labeled real-world data (referred to as “original data”).

FIG.9Ais a flow diagram of a method900for training a machine learning model that can be used for classifying features of interest according to one or more embodiments described herein.FIG.9Bis a flow diagram of a method910for generating synthetic point cloud training data according to one or more embodiments described herein. The method900and/or the method910can be performed by any suitable system or device, such as the processing system500ofFIG.5, the machine learning training and inference system800ofFIG.8, and/or the processing system1100ofFIG.11. The methods900,910are now described with further reference toFIGS.10A-10D and11.

At block902, the processing system500receives original point cloud training data. Original point cloud training data is point cloud data collected by a 3D coordinate measurement device (e.g., the scanner520) about a real-world object (e.g., the object522). For example, the original point cloud training data represents a real-world feature of interest of a real-world object. The original point cloud training data can include point cloud data for multiple objects, multiple features of interest, and/or the like, including combinations and/or multiples thereof.

At block904, the processing system500(e.g., using the ML training engine518, another engine, any suitable processing system, and/or the like, including combinations and/or multiples thereof) generates synthetic point cloud training data. The synthetic point cloud training data is point cloud data that is simulated or generated to replace original point cloud training data but is not collected from a real-world object like the original point cloud training data.FIG.9Bdepicts a method910for generating the synthetic point cloud training data and is now described in more detail with reference toFIGS.10A-10D and11. According to one or more embodiments described herein, synthetic data generation process is performed other than by the ML training engine518used for machine learning. According to one or more embodiments described herein, the machine learning model is trained using a processing system using a graphics processing unit (GPU) (e.g., to increase the training speed); however, the synthetic data can be generated by a system using a central processing unit (CPU) because generating the synthetic training data is less resource (e.g., processor load) intensive than training the machine learning model. It should be appreciated that any suitable system with a CPU (with or without a GPU) can be used to generate data according to one or more embodiments described herein.

With reference toFIG.9B, at block912, the processing system500(e.g., using the ML training engine518, another engine, any suitable processing system, and/or the like, including combinations and/or multiples thereof) generates a two-dimensional (2D) mask of primitives. Primitives, or “geometric primitives,” are basic geometric shapes, such as a point, straight line segment, cure, circles, ellipses, and/or the like, including combinations and/or multiples thereof.FIG.10Adepicts a representation1000that shows masks1001-1004for different primitives. For example, the mask1001is a mask for a circle primitive, the mask1002is a mask for an ellipse primitive, the mask1003is a mask for a rectangle primitive, and the mask1004is a mask for a rounded slot primitive. Other primitives and 2D masks are also possible. According to one or more embodiments described herein, the 2D mask of primitives can be generated using a CPU and/or a GPU.

With continued reference toFIG.9B, at block914, the processing system500(e.g., using the ML training engine518) applies a 2D data augmentation to the 2D mask of primitives to generate augmented images. The 2D data augmentation involves performing manipulations on the 2D masks of primitives. Examples of 2D data augmentation include, for example, distortion, scaling, rotation, and/or the like, including combinations and/or multiples thereof.FIG.10Bdepicts a representation1010showing 2D data augmentation performed on the mask1004(e.g., a rounded slot primitive). Particularly, the representation1010includes augmented images1011-1014having different augmentations applied. For example, the augmented images1011-1014may be different augmentations applied to the mask1004. As can be observed, each of the augmented images1011-1014are substantially round slots but have different transformations relative to the mask1004.

With continued reference toFIG.9B, at block916, the processing system500(e.g., using the ML training engine518), for each of the augmented images (e.g., each of the augmented images1011-1014ofFIG.10B), identifies contours. As shown inFIG.10C, a representation1020includes a contours image1021that corresponds to the augmented image1014. Contours of the augmented image1014are found to create the contours image1021. In an embodiment, identifying the contours can be performed using a clockwise ordering of points. Also at block916, the processing system500(e.g., using the ML training engine518) applies a point-level transformation, which can be selected randomly. Examples of point-level transformations include dropout and gaussian noise. A dropout transformation removes a random set of points and can be applied in a random segment of the sorted points (dropout segment) and, within this segment, can randomly vary from 0, where no point is removed to 1, where all points in segment are removed (dropout ratio). A segment is a part of the point set and its length also can be tuned by a random parameter. If the length is 1, all point are used and the segment matches the whole set (general dropout). Similarly, the gaussian noise can be applied in all points (general noise) and/or a segment (segment noise). In either or both scenarios, the amount of noise (sigma value) is also defined in a parameter. The parameters used in these transformations can be defined manually or using statistics of real-world samples. For example, the amount of dropout ratio and/or the amount of noise defined by sigma value is defined by a previous noise analysis of real samples. One or more the transformations are applied to the sorted points set that resulted from contour extraction and/or the like, including combinations and/or multiples thereof. According to one or more embodiments described herein, a random generator can be used to provide for reproducible results.FIG.10Dshows representations1040,1041,1042that represent the process of applying the point-level transformation. For example, in the three samples, a general gaussian noise was applied. For the representations1040and1041, the sigma value was higher than for the representation1042. The segment1043of representation1040shows an example of noise applied locally in a segment. The amount of noise in that segment is higher than the general noise in the same representation1040. Similarly, the segment1044of representation1041shows a dropout segment where almost all the points in that segment were removed (high dropout ratio within that segment). For the representation1042, a general dropout was applied, which can be seen by the lack of general point density compared to the representations1040and1041.

With continued reference toFIG.9B, at block918, the processing system500(e.g., using the ML training engine518), for each of the output points of point-level transformations (e.g., each of the representation1040-1042ofFIG.10D), adds depth information by analyzing real data and associated real samples statistics (e.g., a real-world sample analysis) to determine depth outlier as a percentage and maximum depth distance. Using results of the analysis, the processing system500can infer the amount of depth to be added in previous transformed points.FIGS.10E-10Gtogether depict a method1050for adding depth information. Real data1051and associated real samples statistics1052can be analyzed to generate depth outlier as a percentage (“depth_outlier_perc”) and maximum depth distance (“max_depth_distance”) as shown in the table1053. The processing system500can then use the information in the table1053to infer depth information for the previous transformed points (shown as images1054that include depth information).

With continued reference toFIG.9A, at block906, the processing system500(e.g., using the ML training engine518) trains the machine learning model using the original point cloud data and the synthetic point cloud training data. Particularly, the machine learning model is trained to generate an output indicating a class of the feature of interest of the object.

It should be understood that the processes depicted inFIGS.9A and9Brepresents an illustration, and that other processes may be added or existing processes may be removed, modified, or rearranged without departing from the scope of the present disclosure.

FIGS.11A-11Ddepict example synthetic point cloud training data according to one or more embodiments described herein. Particularly,FIG.11Adepicts examples of synthetic point cloud training data1101-1106for a circle,FIG.11Bdepicts examples of synthetic point cloud training data1111-1116for an ellipse,FIG.11Cdepicts examples of synthetic point cloud training data1121-1126for a rectangle, andFIG.11Ddepicts examples of synthetic point cloud training data1131-1136for a round slot. These are merely examples of synthetic point cloud training data and should not be construed as limiting to the claims.

It is understood that one or more embodiments described herein is capable of being implemented in conjunction with any other type of computing environment now known or later developed. For example,FIG.12depicts a block diagram of a processing system1200for implementing the techniques described herein. In accordance with one or more embodiments described herein, the processing system1200is an example of a cloud computing node of a cloud computing environment. In examples, processing system1200has one or more central processing units (“processors” or “processing resources” or “processing devices”)1221a,1221b,1221c,etc. (collectively or generically referred to as processor(s)1221and/or as processing device(s)). In aspects of the present disclosure, each processor1221can include a reduced instruction set computer (RISC) microprocessor. Processors1221are coupled to system memory (e.g., random access memory (RAM)1224) and various other components via a system bus1233. Read only memory (ROM)1222is coupled to system bus1233and may include a basic input/output system (BIOS), which controls certain basic functions of processing system1200.

Further depicted are an input/output (I/O) adapter1227and a network adapter1226coupled to system bus1233. I/O adapter1227may be a small computer system interface (SCSI) adapter that communicates with a hard disk1223and/or a storage device1225or any other similar component. I/O adapter1227, hard disk1223, and storage device1225are collectively referred to herein as mass storage1234. Operating system1240for execution on processing system1200may be stored in mass storage1234. The network adapter1226interconnects system bus1233with an outside network1236enabling processing system1200to communicate with other such systems.

A display (e.g., a display monitor)1235is connected to system bus1233by display adapter1232, which may include a graphics adapter to improve the performance of graphics intensive applications and a video controller. In one aspect of the present disclosure, adapters1226,1227, and/or1232may be connected to one or more I/O busses that are connected to system bus1233via an intermediate bus bridge (not shown). Suitable I/O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI). Additional input/output devices are shown as connected to system bus1233via user interface adapter1228and display adapter1232. A keyboard1229, mouse1230, and speaker1231may be interconnected to system bus1233via user interface adapter1228, which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit.

In some aspects of the present disclosure, processing system1200includes a graphics processing unit1237. Graphics processing unit1237is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. In general, graphics processing unit1237is very efficient at manipulating computer graphics and image processing, and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel.

Thus, as configured herein, processing system1200includes processing capability in the form of processors1221, storage capability including system memory (e.g., RAM1224), and mass storage1234, input means such as keyboard1229and mouse1230, and output capability including speaker1231and display1235. In some aspects of the present disclosure, a portion of system memory (e.g., RAM1224) and mass storage1234collectively store the operating system1240to coordinate the functions of the various components shown in processing system1200.

It will be appreciated that one or more embodiments described herein may be embodied as a system, method, or computer program product and may take the form of a hardware embodiment, a software embodiment (including firmware, resident software, micro-code, etc.), or a combination thereof. Furthermore, one or more embodiments described herein may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.