Abstract: A method of the present disclosure includes (a) extracting distinctive features used for respectively distinguishing a plurality of similar attitudes from which images similar to one another are obtained using a simulation model of an object, (b) capturing an object image of the object using a camera, (c) estimating a position and an attitude of the object using the object image, and (d) when the estimated attitude corresponds to one of the plurality of similar attitudes, determining the one of the plurality of similar attitudes as the attitude of the object using the distinctive features.

Abstract: A non-transitory computer readable medium embodies instructions that cause one or more processors to perform a method. The method includes selecting a 3D model corresponding to an object, and generating a first 2D image of the 3D model in a first pose. The method further includes generating a second 2D image of the 3D model in the first pose, the second 2D image having a different texture on the 3D model than the first 2D image. The method further includes, using an algorithm, determining a first location of a first feature on the 3D model in the first 2D image and a second location of a second feature on the 3D model in the second 2D image. The method further includes calculating a difference based on the first location and the second location. The method further includes adjusting parameters representing the algorithm based on the calculated difference.

Abstract: A non-transitory computer readable medium storing instructions to cause one or more processors to acquire an image data sequence containing images of an object in a scene along time and track a pose of the object through an object pose tracking algorithm. The processor may further, acquire a first pose of the object in a first image of the image data sequence, the first pose being a result of tracking a pose of the object through an object pose tracking algorithm, verify the first pose, extract 2D features of the object from the first image when the first pose is verified, and store a training dataset containing the extracted 2D features and the corresponding verified first pose in the one or more memories or other one or more memories.

Abstract: Compositions, systems, and methods for producing nanoalloys and/or nanocomposites using tandem laser ablation synthesis in solution-galvanic replacement reaction (LASiS-GRR) are disclosed. The method may include disposing a first metal composition within a reaction cell, adding a quantity of a second metal composition into the reaction cell, ablating, with a laser, the first metal composition disposed in the quantity of the second metal composition within the reaction cell, and tuning one or more reaction parameter and/or one or more functional parameter during the tandem LASiS-GRR in order to tailor at least one characteristic of the metal nanoalloy and/or the metal nanocomposite.

Abstract: A non-transitory computer readable medium embodies instructions that cause one or more processors to perform a method. The method includes selecting a 3D model corresponding to an object, and generating a first 2D image of the 3D model in a first pose. The method further includes generating a second 2D image of the 3D model in the first pose, the second 2D image having a different texture on the 3D model than the first 2D image. The method further includes, using an algorithm, determining a first location of a first feature on the 3D model in the first 2D image and a second location of a second feature on the 3D model in the second 2D image. The method further includes calculating a difference based on the first location and the second location. The method further includes adjusting parameters representing the algorithm based on the calculated difference.

Abstract: Compositions, systems, and methods for producing nanoalloys and/or nanocomposites using tandem laser ablation synthesis in solution-galvanic replacement reaction (LASiS-GRR) are disclosed. The method may include disposing a first metal composition within a reaction cell, adding a quantity of a second metal composition into the reaction cell, ablating, with a laser, the first metal composition disposed in the quantity of the second metal composition within the reaction cell, and tuning one or more reaction parameter and/or one or more functional parameter during the tandem LASiS-GRR in order to tailor at least one characteristic of the metal nanoalloy and/or the metal nanocomposite.

Abstract: A non-transitory computer readable medium embodies instructions that cause one or more processors to perform a method. The method includes selecting a 3D model corresponding to an object. The method further includes generating domain-adapted images of the 3D model, the domain-adapted images representing the 3D model at corresponding poses. The method further includes acquiring 2D projections of 3D points on a 3D bounding box defined around the 3D model at the corresponding poses. The method further includes training an algorithm model to learn correspondences between the generated images and the corresponding 2D projections. The method further includes storing, in a memory, parameters representing the algorithm model.

Abstract: A non-transitory computer readable medium embodies instructions that cause one or more processors to perform a method for training an object detection algorithm. The method includes: (a) selecting a 3D model corresponding to an object; (b) acquiring images of the 3D model, the images being obtained by rendering the 3D model at respective poses; (c) acquiring 2D projections of 3D points on the 3D model at the respective poses; and (d) storing, in a memory, an association between the acquired 2D projections and the respective poses.

Abstract: An exemplary method includes generating a first image containing a model image based on a 3D model at a pose. Second images are acquired containing an image not containing the model image. Training image patches are extracted from the first and second images, each training image patch being associated with a class representing whether the corresponding image patch contains at least a part of the model image. An algorithm model is trained with the training image patches and the respective classes to derive the model image's position relative to the first image. Parameters defining the trained algorithm model are stored. Another exemplary method includes a camera acquiring an image containing an object in a scene. An enhancement filter is applied to the acquired image. Image patches are extracted from the filtered image. The object's position is determined in the image by applying the trained algorithm model to the image patches.

Abstract: A non-transitory computer readable medium storing instructions to cause one or more processors to acquire an image data sequence containing images of an object in a scene along time and track a pose of the object through an object pose tracking algorithm. The processor may further, acquire a first pose of the object in a first image of the image data sequence, the first pose being a result of tracking a pose of the object through an object pose tracking algorithm, verify the first pose, extract 2D features of the object from the first image when the first pose is verified, and store a training dataset containing the extracted 2D features and the corresponding verified first pose in the one or more memories or other one or more memories.

Abstract: A non-transitory computer readable medium embodies instructions that cause one or more processors to perform a method. The method includes: (A) receiving, in one or more memories, a 3D model corresponding to an object, and (B) setting a depth sensor characteristic data set for a depth sensor for use in detecting a pose of the object in a real scene. The method also includes (C) generating blurred 2.5D representation data of the 3D model for at least one view around the 3D model based on the 3D model and the depth sensor characteristic data set, to generate, on the basis of the 2.5D representation data, training data for training an object detection algorithm, and (D) storing the training data in one or more memories.

Abstract: A non-transitory computer readable medium storing instructions to cause one or more processors to acquire, from a camera or one or more memory storing an image data sequence captured by the camera, the image data sequence containing images of an object in a scene along a time. The instructions further cause the one or more processors to track a pose of the object through an object pose tracking algorithm and during the tracking of the pose, acquire a first pose of the object in a first image of the image data sequence. The instructions further cause the one or more processor to, during the tracking, extract two-dimensional (2D) features of the object from the first image, and store a training dataset containing the extracted 2D features and the corresponding first pose in the one or more memories or other one or more memories.

Abstract: A head-mounted display, a method, and a non-transitory computer readable medium are provided. An embodiment of a method for obtaining training sample views of an object includes the step of storing, in a memory, multiple views of an object. The method also includes the step of deriving similarity scores between adjacent views and then a sampling density is varied based on the similarity scores.

Abstract: A method includes acquiring, from a camera, an image data sequence of a real object in a real scene and performing a first template-matching on an image frame in the image data sequence using intensity-related data sets stored in one or more memories to generate response maps. The intensity-related data sets represent an intensity distribution of a reference object from respective viewpoints. The reference object corresponds to the real object. A candidate region of interest is determined for the real object in the image frame based on the response maps, and second template-matching is performed on the candidate region of interest using shape-related feature data sets stored in one or more memories to derive a pose of the real object. The shape-related feature data sets represent edge information of the reference object from the respective viewpoints.

Abstract: A non-transitory computer readable medium embodies instructions that cause one or more processors to perform a method. The method includes selecting a 3D model corresponding to an object. The method further includes generating domain-adapted images of the 3D model, the domain-adapted images representing the 3D model at corresponding poses. The method further includes acquiring 2D projections of 3D points on a 3D bounding box defined around the 3D model at the corresponding poses. The method further includes training an algorithm model to learn correspondences between the generated images and the corresponding 2D projections. The method further includes storing, in a memory, parameters representing the algorithm model.

Abstract: Compositions, systems, and methods for producing nanoalloys and/or nanocomposites using tandem laser ablation synthesis in solution-galvanic replacement reaction (LASiS-GRR) are disclosed. The method may include disposing a first metal composition within a reaction cell, adding a quantity of a second metal composition into the reaction cell, ablating, with a laser, the first metal composition disposed in the quantity of the second metal composition within the reaction cell, and tuning one or more reaction parameter and/or one or more functional parameter during the tandem LASiS-GRR in order to tailor at least one characteristic of the metal nanoalloy and/or the metal nanocomposite.

Abstract: A non-transitory computer readable medium embodies instructions that cause one or more processors to perform a method. The method includes: (A) receiving, in one or more memories, a 3D model corresponding to an object, and (B) setting a depth sensor characteristic data set for a depth sensor for use in detecting a pose of the object in a real scene. The method also includes (C) generating blurred 2.5D representation data of the 3D model for at least one view around the 3D model based on the 3D model and the depth sensor characteristic data set, to generate, on the basis of the 2.5D representation data, training data for training an object detection algorithm, and (D) storing the training data in one or more memories.

Abstract: A head-mounted display, a method, and a non-transitory computer readable medium are provided. An embodiment of a method for obtaining training sample views of an object includes the step of storing, in a memory, multiple views of an object. The method also includes the step of deriving similarity scores between adjacent views and then a sampling density is varied based on the similarity scores.

Abstract: A method includes acquiring, from a camera, an image data sequence of a real object in a real scene and performing a first template-matching on an image frame in the image data sequence using intensity-related data sets stored in one or more memories to generate response maps. The intensity-related data sets represent an intensity distribution of a reference object from respective viewpoints. The reference object corresponds to the real object. A candidate region of interest is determined for the real object in the image frame based on the response maps, and second template-matching is performed on the candidate region of interest using shape-related feature data sets stored in one or more memories to derive a pose of the real object. The shape-related feature data sets represent edge information of the reference object from the respective viewpoints.

Abstract: Compositions, systems, and methods for producing nanoalloys and/or nanocomposites using tandem laser ablation synthesis in solution-galvanic replacement reaction (LASiS-GRR) are disclosed. The method may include disposing a first metal composition within a reaction cell, adding a quantity of a second metal composition into the reaction cell, ablating, with a laser, the first metal composition disposed in the quantity of the second metal composition within the reaction cell, and tuning one or more reaction parameter and/or one or more functional parameter during the tandem LASiS-GRR in order to tailor at least one characteristic of the metal nanoalloy and/or the metal nanocomposite.