Abstract: Disclosed is an imaging system that use point cloud data point depth values and/or positional data to accurately differentiate and extract features from a point cloud representation of a three-dimensional (“3D”) environment without loss of extremely fine details of the extracted features, and without reliance on green screens or other chroma keying techniques. The imaging system differentiate and extract a set of data points, that represent a particular feature in the 3D environment, from other data points of a first point cloud based on the positional data of the set of data points being within depth values specified for feature extraction. The imaging system may generate the 3D environment with at least one visual effect by inserting the set of data points into a second point cloud with one or more data points of the second point cloud differing from the data points of the first point cloud.

Abstract: Disclosed are systems and methods for defining, bonding, and editing point cloud data points with primitives. In particular, a system may receive a point cloud with data points that are defined by positional and non-positional elements. The system may associate primitives to each data point based on a material or a property associated with the surface, feature, or article at a position in an imaged three-dimensional (“3D”) environment that corresponds to a particular position of the data point in the point cloud. The system may receive input for a first adjustment to at least one of the data point elements, and may edit point cloud by defining a second adjustment that modifies the first adjustment according to a rule of a particular primitive, and by applying the second adjustment to the at least one element of a first set of data points that are associated with the particular primitive.

Abstract: Disclosed is a system that receives a point cloud, and that generates a Bounding Volume Hierarchy (“BVH”) based on the point cloud data points. The BVH includes leaf nodes and parent nodes at one or more levels above the leaf nodes. The leaf nodes correspond to the point cloud data points. The system may receive input for adjusting a first set of elements of data points that are identified based on values specified for a second set of elements, and may locate those data points by traversing the BVH to arrive at a particular parent node that encompasses the values specified for the second set of elements. The system may then modify, based on the input, the first set of elements of a set of data points that correspond to a set of leaf nodes from the BVH that are directly or indirectly linked to the particular parent node.

Abstract: Disclosed is a system and method for rendering point clouds via a hybrid data point and construct visualization. The system receives a point cloud of a three-dimensional (“3D”) environment, and differentiates a first set of the point cloud data points from a second set of the data points based on a position of each data point relative to a specified render position. The system generates a first visualization from values of each of the first set of data points, and a second visualization from values of a set of constructs that replace the second set of data points. Each construct has a polygonal shape and a singular set of values defined from the values of two or more of the second set of data points. The system presents a final render of the 3D environment from the render position by combining the first visualization with the second visualization.

Abstract: Provided is a multispectral imaging device for providing precise tracking and verification. The imaging device may configure a first filter for a sensor, and may determine first spectral properties of a target object based on a first image of the target object generated from visible light passing through the first filter onto the sensor. The imaging device may configure a different second filter for the sensor, and may determine second spectral properties of the target object based on a second image of the target object generated from the non-visible light passing through the second filter onto the sensor. The imaging device may align the second spectral properties of the second image with the first spectral properties of the first image, and may present the first spectral properties with the second spectral properties in a single composite image of the target object.

Abstract: Provided is a multispectral imaging device for providing precise tracking and verification. The imaging device may configure a first filter for a sensor, and may determine first spectral properties of a target object based on a first image of the target object generated from visible light passing through the first filter onto the sensor. The imaging device may configure a different second filter for the sensor, and may determine second spectral properties of the target object based on a second image of the target object generated from the non-visible light passing through the second filter onto the sensor. The imaging device may align the second spectral properties of the second image with the first spectral properties of the first image, and may present the first spectral properties with the second spectral properties in a single composite image of the target object.

Abstract: A comparator may automatically detect and quantify subtle and/or microscopic variance to a feature of a three-dimensional (“3D”) object in a reproducible manner based on point cloud imaging of that 3D object. The comparator may isolate a first set of data points, that represent the object feature at a first time, in a reference point cloud, and may isolate a second set of data points, that represent the same but altered object feature at a different second time, in a non-reference point cloud. The comparator may detect variance between positional values and visual characteristic values of the second set of data points and the corresponding positional values and visual characteristic values of the first set of data points, and may quantify a change occurring to the object feature between the first time and the second time based on a mapping of the variance to a particular unit of measure.

Abstract: Disclosed is a system for efficiently accessing a point cloud via a multi-tree deconstruction of the point cloud. The system may receive the point cloud, may differentiate different sets of data points from the point cloud using differentiation criteria, and may generate different trees with each tree having leaf nodes corresponding to one of the differentiated sets of data points and parent nodes defined according to commonality in values of two or more leaf nodes. The system may receive a request to render the 3D environment, load a first tree into memory, generate a first image from the first tree data points, flush the first tree from the memory, load a second tree into the memory, generate a second image from the second tree data points, and present a composite image by combining at least the first image with the second image.

Abstract: A sensor sphere may measure light and/or other properties of an environment from a multitude of angles and/or perspectives. A rendering system may obtain measurements that the sensor sphere generates as a result of measuring light in the environment from a common position and the multitude of angles and/or perspectives. The rendering system may receive image(s) that capture the environment from a first perspective, may determine a first set of the measurements that measure the lighting from the first perspective, may determine a second set of the measurements that measure the lighting from a different second perspective, may illuminate the environment from the second perspective by adjusting the lighting of the environment according to a difference between the second set of measurements and the first set of measurements, and may render the environment from the second perspective with the adjusted lighting.

Abstract: Disclosed is a system and method for rendering point clouds via a hybrid data point and construct visualization. The system receives a point cloud of a three-dimensional (“3D”) environment, and differentiates a first set of the point cloud data points from a second set of the data points based on a position of each data point relative to a specified render position. The system generates a first visualization from values of each of the first set of data points, and a second visualization from values of a set of constructs that replace the second set of data points. Each construct has a polygonal shape and a singular set of values defined from the values of two or more of the second set of data points. The system presents a final render of the 3D environment from the render position by combining the first visualization with the second visualization.

Abstract: Disclosed is a system for efficiently accessing a point cloud via a multi-tree deconstruction of the point cloud. The system may receive the point cloud, may differentiate different sets of data points from the point cloud using differentiation criteria, and may generate different trees with each tree having leaf nodes corresponding to one of the differentiated sets of data points and parent nodes defined according to commonality in values of two or more leaf nodes. The system may receive a request to render the 3D environment, load a first tree into memory, generate a first image from the first tree data points, flush the first tree from the memory, load a second tree into the memory, generate a second image from the second tree data points, and present a composite image by combining at least the first image with the second image.

Abstract: A comparator may automatically detect and quantify subtle and/or microscopic variance to a feature of a three-dimensional (“3D”) object in a reproducible manner based on point cloud imaging of that 3D object. The comparator may isolate a first set of data points, that represent the object feature at a first time, in a reference point cloud, and may isolate a second set of data points, that represent the same but altered object feature at a different second time, in a non-reference point cloud. The comparator may detect variance between positional values and visual characteristic values of the second set of data points and the corresponding positional values and visual characteristic values of the first set of data points, and may quantify a change occurring to the object feature between the first time and the second time based on a mapping of the variance to a particular unit of measure.

Abstract: Provided is a three-dimensional (“3D”) analysis tool or device for generating visualizations for attributes in different multi-plane slice of a point cloud or 3D image. The device may receive a point cloud with a plurality of data points that are distributed across a plurality of different planes and that collectively produce a 3D image. The device may select a set of the plurality of data points that are located within a particular slice of the point cloud, may generate a visualization from one or more attributes of the set of data points, and may present the visualization of the one or more attributes for the set of data points in a different format than a rendering of the set of data points.

Abstract: A sensor sphere may measure light and/or other properties of an environment from a multitude of angles and/or perspectives. A rendering system may obtain measurements that the sensor sphere generates as a result of measuring light in the environment from a common position and the multitude of angles and/or perspectives. The rendering system may receive image(s) that capture the environment from a first perspective, may determine a first set of the measurements that measure the lighting from the first perspective, may determine a second set of the measurements that measure the lighting from a different second perspective, may illuminate the environment from the second perspective by adjusting the lighting of the environment according to a difference between the second set of measurements and the first set of measurements, and may render the environment from the second perspective with the adjusted lighting.

Abstract: Provided is a multispectral imaging device for providing precise tracking and verification. The imaging device may configure a first filter for a sensor, and may determine first spectral properties of a target object based on a first image of the target object generated from visible light passing through the first filter onto the sensor. The imaging device may configure a different second filter for the sensor, and may determine second spectral properties of the target object based on a second image of the target object generated from the non-visible light passing through the second filter onto the sensor. The imaging device may align the second spectral properties of the second image with the first spectral properties of the first image, and may present the first spectral properties with the second spectral properties in a single composite image of the target object.

Abstract: A device is provided for encrypting and/or decrypting a point cloud having a plurality of data points that collectively produce a three-dimensional (“3D”) image. Each data point may have a set of elements with values that define a position of the data point in 3D space and visual characteristics of the data point. Encrypting the point cloud may include deterministically a set of data point to encrypt, and deterministically changing the data point element values of the selected data points so that the 3D image produced by the encrypted data points is different than the 3D produced from the unencrypted data points. Decrypting the resulting encrypted point cloud may include deterministically reselecting the encrypted data points using an encryption key, and deterministically reversing the changes made to the data point element values of the selected data points based on transformations that are specified as part of the encryption key.

Abstract: Provided is a system for structured and controlled movement and viewing within a point cloud. The system may generate or obtain a plurality of data points and one or more waypoints for the point cloud, present a first subset of the plurality of data points in a field-of-view of a camera at an initial position and an initial orientation of a first waypoint, change the camera field-of-view from at least one of (i) the initial position to a modified position within a volume of positions defined by orientation controls of the first waypoint or (ii) the initial orientation to a modified orientation within a range of orientations defined by the orientation controls of the first waypoint, and may present a second subset of the plurality of data points in the camera field-of-view at one or more of the modified position and the modified orientation.

Abstract: A device is provided for encrypting and/or decrypting a point cloud having a plurality of data points that collectively produce a three-dimensional (“3D”) image. Each data point may have a set of elements with values that define a position of the data point in 3D space and visual characteristics of the data point. Encrypting the point cloud may include deterministically a set of data point to encrypt, and deterministically changing the data point element values of the selected data points so that the 3D image produced by the encrypted data points is different than the 3D produced from the unencrypted data points. Decrypting the resulting encrypted point cloud may include deterministically reselecting the encrypted data points using an encryption key, and deterministically reversing the changes made to the data point element values of the selected data points based on transformations that are specified as part of the encryption key.

Abstract: A comparator may automatically detect and quantify subtle and/or microscopic variance to a feature of a three-dimensional (“3D”) object in a reproducible manner based on point cloud imaging of that 3D object. The comparator may isolate a first set of data points, that represent the object feature at a first time, in a reference point cloud, and may isolate a second set of data points, that represent the same but altered object feature at a different second time, in a non-reference point cloud. The comparator may detect variance between positional values and visual characteristic values of the second set of data points and the corresponding positional values and visual characteristic values of the first set of data points, and may quantify a change occurring to the object feature between the first time and the second time based on a mapping of the variance to a particular unit of measure.

Abstract: A device is provided for encrypting and/or decrypting a point cloud having a plurality of data points that collectively produce a three-dimensional (“3D”) image. Each data point may have a set of elements with values that define a position of the data point in 3D space and visual characteristics of the data point. Encrypting the point cloud may include deterministically a set of data point to encrypt, and deterministically changing the data point element values of the selected data points so that the 3D image produced by the encrypted data points is different than the 3D produced from the unencrypted data points. Decrypting the resulting encrypted point cloud may include deterministically reselecting the encrypted data points using an encryption key, and deterministically reversing the changes made to the data point element values of the selected data points based on transformations that are specified as part of the encryption key.