Abstract: Systems and methods for obtaining 3D point-level segmentation of 3D point clouds in accordance with various embodiments of the invention are disclosed. One embodiment includes: at least one processor, and a memory containing a segmentation pipeline application. In addition, the segmentation pipeline application configures the at least one processor to: pre-process a 3D point cloud to group 3D points; provide the groups of 3D points to a 3D neural network to generate initial label predictions for the groups of 3D points; interpolate label predictions for individual 3D points based upon initial label predictions for at least two neighboring groups of 3D points including the group of 3D points to which a given individual 3D point belongs; refine the label predictions using a graph neural network; and output a segmented 3D point cloud.

Abstract: Systems and methods for performing three-dimensional semantic parsing of indoor spaces in accordance with embodiments of the invention are disclosed. In one embodiment, a method includes receiving input data representing a three-dimensional space, determining disjointed spaces within the received data by generating a density histogram on each of a plurality of axes, determining space dividers based on the generated density histogram, and dividing the point cloud data into segments based on the determined space dividers, and determining elements in the disjointed spaces by aligning the disjointed spaces within the point cloud data along similar axes to create aligned versions of the disjointed spaces normalizing the aligned version of the disjointed spaces into the aligned version of the disjointed spaces, determining features in the disjointed spaces, generating at least one detection score, and filtering the at least one detection score to determine a final set of determined elements.

Abstract: Devices, systems, and methods obtain an object model, add the object model to a synthetic scene, add a texture to the object model, add a background plane to the synthetic scene, add a support plane to the synthetic scene, add a background image to one or both of the background plane and the support plane, and generate a pair of images based on the synthetic scene, wherein a first image in the pair of images is a depth image of the synthetic scene, and wherein a second image in the pair of images is a color image of the synthetic scene.

Abstract: Systems and methods for obtaining 3D point-level segmentation of 3D point clouds in accordance with various embodiments of the invention are disclosed. One embodiment includes: at least one processor, and a memory containing a segmentation pipeline application. In addition, the segmentation pipeline application configures the at least one processor to: pre-process a 3D point cloud to group 3D points; provide the groups of 3D points to a 3D neural network to generate initial label predictions for the groups of 3D points; interpolate label predictions for individual 3D points based upon initial label predictions for at least two neighboring groups of 3D points including the group of 3D points to which a given individual 3D point belongs; refine the label predictions using a graph neural network; and output a segmented 3D point cloud.

Abstract: Devices, systems, and methods obtain an object model, add the object model to a synthetic scene, add a texture to the object model, add a background plane to the synthetic scene, add a support plane to the synthetic scene, add a background image to one or both of the background plane and the support plane, and generate a pair of images based on the synthetic scene, wherein a first image in the pair of images is a depth image of the synthetic scene, and wherein a second image in the pair of images is a color image of the synthetic scene.

Abstract: Systems and methods for performing three-dimensional semantic parsing of indoor spaces in accordance with embodiments of the invention are disclosed. In one embodiment, a method includes receiving input data representing a three-dimensional space, determining disjointed spaces within the received data by generating a density histogram on each of a plurality of axes, determining space dividers based on the generated density histogram, and dividing the point cloud data into segments based on the determined space dividers, and determining elements in the disjointed spaces by aligning the disjointed spaces within the point cloud data along similar axes to create aligned versions of the disjointed spaces normalizing the aligned version of the disjointed spaces into the aligned version of the disjointed spaces, determining features in the disjointed spaces, generating at least one detection score, and filtering the at least one detection score to determine a final set of determined elements.