Abstract: A system for context-aware decision making of an autonomous agent includes a computing system having a context selector and a map. A method for context-aware decision making of an autonomous agent includes receiving a set of inputs, determining a context associated with an autonomous agent based on the set of inputs, and optionally any or all of: labeling a map; selecting a learning module (context-specific learning module) based on the context; defining an action space based on the learning module; selecting an action from the action space; planning a trajectory based on the action 8260; and/or any other suitable processes.

Abstract: A system for context-aware decision making of an autonomous agent includes a computing system having a context selector and a map. A method for context-aware decision making of an autonomous agent includes receiving a set of inputs, determining a context associated with an autonomous agent based on the set of inputs, and optionally any or all of: labeling a map; selecting a learning module (context-specific learning module) based on the context; defining an action space based on the learning module; selecting an action from the action space; planning a trajectory based on the action S260; and/or any other suitable processes.

Abstract: A system for data-driven, modular decision making and trajectory generation includes a computing system. A method for data-driven, modular decision making and trajectory generation includes: receiving a set of inputs; selecting a learning module such as a deep decision network and/or a deep trajectory network from a set of learning modules; producing an output based on the learning module; repeating any or all of the above processes; and/or any other suitable processes. Additionally or alternatively, the method can include training any or all of the learning modules; validating one or more outputs; and/or any other suitable processes and/or combination of processes.

Abstract: A system for data-driven, modular decision making and trajectory generation includes a computing system. A method for data-driven, modular decision making and trajectory generation includes: receiving a set of inputs; selecting a learning module such as a deep decision network and/or a deep trajectory network from a set of learning modules; producing an output based on the learning module; repeating any or all of the above processes; and/or any other suitable processes. Additionally or alternatively, the method can include training any or all of the learning modules; validating one or more outputs; and/or any other suitable processes and/or combination of processes.

Abstract: A system for deterministic trajectory selection based on uncertainty estimation includes a set of one or more computing systems. A method for deterministic trajectory selection includes receiving a set of inputs; determining a set of outputs; determining uncertainty parameters associated with any or all of the set of inputs and/or any or all of the set of outputs; and evaluating the uncertainty parameters and optionally triggering a process and/or action in response.

Abstract: A system for data-driven, modular decision making and trajectory generation includes a computing system. A method for data-driven, modular decision making and trajectory generation includes: receiving a set of inputs; selecting a learning module such as a deep decision network and/or a deep trajectory network from a set of learning modules; producing an output based on the learning module; repeating any or all of the above processes; and/or any other suitable processes. Additionally or alternatively, the method can include training any or all of the learning modules; validating one or more outputs; and/or any other suitable processes and/or combination of processes.

Abstract: A system for context-aware decision making of an autonomous agent includes a computing system having a context selector and a map. A method for context-aware decision making of an autonomous agent includes receiving a set of inputs, determining a context associated with an autonomous agent based on the set of inputs, and optionally any or all of: labeling a map; selecting a learning module (context-specific learning module) based on the context; defining an action space based on the learning module; selecting an action from the action space; planning a trajectory based on the action S260; and/or any other suitable processes.

Abstract: A method for estimating tightly enclosing bounding boxes by a computing system includes: controlling a scanning system including one or more depth cameras to capture visual information of the scene including one or more objects; detecting the one or more objects of the scene based on the visual information; singulating each the one or more objects from the frame of the scene to generate one or more 3D models corresponding to the one or more objects, the one or more 3D models including a partial 3D model of a corresponding one of the one or more objects; extrapolating a more complete 3D model of the corresponding one of the one or more objects based on the partial 3D model; and estimating a tightly enclosing bounding box of the corresponding one of the one or more objects based on the more complete 3D model.

Abstract: A method for identifying and tracking objects includes: capturing one or more 3-D models of one or more objects in a scene using a three-dimensional (3-D) scanning system, the one or more 3-D models including color and geometry information of the one or more objects; and computing, by an analysis agent, one or more descriptors of the one or more 3-D models, each descriptor corresponding to a fixed-length feature vector; and retrieving metadata identifying the one or more objects based on the one or more descriptors.

Abstract: Systems and methods for image alignment are disclosed. A method includes: determining a first set of patches for a first image and a second set of patches for a second image, wherein each patch in the first and second sets of patches comprises a portion of the first and second image, respectively; determining a set of possible match pairings of a patch from the first set and a patch from the second set; for each match pairing in the set of possible match pairings, computing a transformation operation to align the patch from the first set of patches and the patch from the second set of patches in the match pairing; grouping the match pairings into clusters based on parameters of the transformation operations of the match pairings; and, performing the transformation operation corresponding to at least one cluster to align the first image to the second image.

Abstract: A method for computing dimensions of an object in a scene includes: controlling, by a processor, a depth camera system to capture at least a frame of the scene, the frame including a color image and a depth image arranged in a plurality of pixels; detecting, by the processor, an object in the frame; determining, by the processor, a ground plane in the frame, the object resting on the ground plane; computing, by the processor, a rectangular outline bounding a projection of a plurality of pixels of the object onto the ground plane; computing, by the processor, a height of the object above the ground plane; and outputting, by the processor, computed dimensions of the object in accordance with a length and a width of the rectangular outline and the height.

Abstract: A method for identifying and tracking objects includes: capturing one or more 3-D models of one or more objects in a scene using a three-dimensional (3-D) scanning system, the one or more 3-D models including color and geometry information of the one or more objects; and computing, by an analysis agent, one or more descriptors of the one or more 3-D models, each descriptor corresponding to a fixed-length feature vector; and retrieving metadata identifying the one or more objects based on the one or more descriptors.

Abstract: A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.

Abstract: A method for detecting defects in objects includes: controlling, by a processor, one or more depth cameras to capture a plurality of depth images of a target object; computing, by the processor, a three-dimensional (3-D) model of the target object using the depth images; rendering, by the processor, one or more views of the 3-D model; computing, by the processor, a descriptor by supplying the one or more views of the 3-D model to a convolutional stage of a convolutional neural network; supplying, by the processor, the descriptor to a defect detector to compute one or more defect classifications of the target object; and outputting the one or more defect classifications of the target object.

Abstract: A keypoint detection system includes: a camera system including at least one camera; and a processor and memory, the processor and memory being configured to: receive an image captured by the camera system; compute a plurality of keypoints in the image using a convolutional neural network including: a first layer implementing a first convolutional kernel; a second layer implementing a second convolutional kernel; an output layer; and a plurality of connections between the first layer and the second layer and between the second layer and the output layer, each of the connections having a corresponding weight stored in the memory; and output the plurality of keypoints of the image computed by the convolutional neural network.

Abstract: A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.

Abstract: A method for detecting a defect in an object includes: capturing, by one or more depth cameras, a plurality of partial point clouds of the object from a plurality of different poses with respect to the object; merging, by a processor, the partial point clouds to generate a merged point cloud; computing, by the processor, a three-dimensional (3D) multi-view model of the object; detecting, by the processor, one or more defects of the object in the 3D multi-view model; and outputting, by the processor, an indication of the one or more defects of the object.

Abstract: A biometric input system includes: a biometric sensor, configured to generate a biometric image comprising features of an input biometric object; and a processing system, configured to receive the biometric image, generate a feature map from the biometric image, perform a distance transform on the feature map, and determine an attribute of the biometric image from the distance transform. The processing system is further configured to compare the determined attribute of the biometric image with corresponding attributes of enrolled biometric images, and, based on the comparison, to eliminate enrolled biometric images from a matching process for the biometric image, wherein the matching process is performed after the elimination and comprises comparison of the biometric image to remaining enrolled biometric images based on one or more attributes of the biometric image other than the determined attribute.

Abstract: A three-dimensional scanning system includes: a camera configured to capture images; a processor; and memory coupled to the camera and the processor, the memory being configured to store: the images captured by the camera; and instructions that, when executed by the processor, cause the processor to: control the camera to capture one or more initial images of a subject from a first pose of the camera; compute a guidance map in accordance with the one or more initial images to identify one or more next poses; control the camera to capture one or more additional images from at least one of the one or more next poses; update the guidance map in accordance with the one or more additional images; and output the images captured by the camera to generate a three-dimensional model.

Abstract: Systems and methods for image alignment are disclosed. A method includes: determining a first set of patches for a first image and a second set of patches for a second image, wherein each patch in the first and second sets of patches comprises a portion of the first and second image, respectively; determining a set of possible match pairings of a patch from the first set and a patch from the second set; for each match pairing in the set of possible match pairings, computing a transformation operation to align the patch from the first set of patches and the patch from the second set of patches in the match pairing; grouping the match pairings into clusters based on parameters of the transformation operations of the match pairings; and, performing the transformation operation corresponding to at least one cluster to align the first image to the second image.