Abstract: Disclosed herein are methods and systems for using artificial intelligence modeling techniques to train and execute an artificial intelligence model to analyze camera feed received from an ego to generate an occupancy data indicating whether different voxels within the ego's surroundings are occupied by an object having mass. A method comprises inputting, using a camera of an ego object, image data of a space around the ego object into an artificial intelligence model; predicting, by executing the artificial intelligence model, an occupancy attribute of a plurality of voxels; and generating a dataset based on the plurality of voxels and their corresponding occupancy attribute.

Abstract: A backpack having one or more flaps that are configured with a series of magnets is utilized to better secure utensils in place. A flap may be configured with external pouches, positioned on top of a base, inside which utensils are placed. Vertically and linearly aligned with the external pouches are magnets positioned within pockets of a magnet pouch positioned behind the base. The magnet pouch may be entirely sealed, covered, and inaccessible from the base's cover, such that utensils, such as knives, can be attracted to the magnets but the utensils only contacts the base layer. Such an arrangement and utilization of magnets can be utilized internally to the backpack, but also externally for external pockets such as, for example, the side, front, or rear pockets.

Abstract: Disclosed herein are methods and systems for using artificial intelligence modeling techniques to train and execute an artificial intelligence model to analyze camera feed received from an ego to generate an occupancy data indicating whether different voxels within the ego's surroundings are occupied by an object having mass. A method comprises inputting, using a camera of an ego object, image data of a space around the ego object into an artificial intelligence model; predicting, by executing the artificial intelligence model, an occupancy attribute of a plurality of voxels; and generating a dataset based on the plurality of voxels and their corresponding occupancy attribute.

Abstract: Disclosed herein are methods and system of implementing an AI-enabled high-fidelity occupancy network, allowing for the prediction of signed distances of voxelized objects in a 3D space surrounding an ego, thereby facilitating enhanced object shape refinement. Through the utilization of camera feeds only, an AI model accurately calculates signed distance values for various voxels in the 3D space. The predicted values can also be rendered by translating signed distance field values into image layers and subsequently stacking the layers to form a 3D representation of the space surrounding the ego. Furthermore, the system can predict and display various ground markings, expanding its functionality beyond traditional lane detection.

Abstract: Disclosed herein are methods and system of implementing an AI-enabled high-fidelity occupancy network, allowing for the prediction of signed distances of voxelized objects in a 3D space surrounding an ego, thereby facilitating enhanced object shape refinement. Through the utilization of camera feeds only, an AI model accurately calculates signed distance values for various voxels in the 3D space. The predicted values can also be rendered by translating signed distance field values into image layers and subsequently stacking the layers to form a 3D representation of the space surrounding the ego. Furthermore, the system can predict and display various ground markings, expanding its functionality beyond traditional lane detection.

Abstract: Disclosed herein are methods and systems for using artificial intelligence modeling techniques to train and execute an artificial intelligence model to analyze camera feed received from an ego to generate an occupancy data indicating whether different voxels within the ego's surroundings are occupied by an object having mass. A method comprises inputting, using a camera of an ego object, image data of a space around the ego object into an artificial intelligence model; predicting, by executing the artificial intelligence model, an occupancy attribute of a plurality of voxels; and generating a dataset based on the plurality of voxels and their corresponding occupancy attribute.

Abstract: A backpack having multiple pouches on an inside flap can have a fastener which horizontally extends across the flap and which secures kitchen utensils, such as knives, in place. The flap can have multiple aligned pouches which extend laterally across a bottom portion of the flap. A distance above the flaps is a base fastener which extends on a base from one lateral end of the flap to the opposite lateral end. When the handles of kitchen utensils, such as knives, are placed inside respective pouches, the sharpened section of each knife is at least partially exposed. A strap fastener is attached at one lateral end of the flap and removably engages with the base fastener. The strap fastener secures the knives in place by overlapping with the knives and engaging with the exposed portions of the base fastener.

Abstract: An illustrative volumetric capture system accesses a machine learning model associated with bodies of a particular body type, as well as a two-dimensional (2D) image captured by a capture device located at a real-world scene. The 2D image depicts a body of the particular body type that is present at the real-world scene. Using the machine learning model and based on the 2D image, the volumetric capture system identifies a 2D joint location, from a perspective of the capture device, of a particular joint of the body. The volumetric capture system also generates a three-dimensional (3D) reference model of the body that represents the particular joint of the body at a 3D joint location that is determined based on the 2D joint location identified using the machine learning model. Corresponding methods and systems are also disclosed.

Abstract: An exemplary three-dimensional (3D) simulation system accesses a two-dimensional (2D) video image captured by a video capture device and that depicts a bounded real-world scene and a real-world object present within the bounded real-world scene. The 3D simulation system accesses respective 3D models of the bounded real-world scene and the real-world object. Based on the 2D video image, the 3D simulation system tracks a spatial characteristic of the real-world object relative to the bounded real-world scene. Based on the tracked spatial characteristic of the real-world object and the 3D models of the bounded real-world scene and the real-world object, the 3D simulation system generates a 3D simulation of the bounded real-world scene within which the real-world object is simulated in accordance with the tracked spatial characteristic of the real-world object. Corresponding methods and systems are also disclosed.

Abstract: A method includes receiving two-dimensional video streams from a plurality of cameras, the two-dimensional video streams including multiple angles of a sporting event. The method further includes determining boundaries of the sporting event from the two-dimensional video streams. The method further includes identifying a location of a sporting object during the sporting event. The method further includes identifying one or more players in the sporting event. The method further includes identifying poses of each of the one or more players during the sporting event. The method further includes generating a three-dimensional model of the sporting event based on the boundaries of the sporting event, the location of the sporting object during the sporting event, and the poses of each of the one or more players during the sporting event. The method further includes generating a simulation of the three-dimensional model.

Abstract: A backpack having multiple pouches on an inside flap can have a fastener which horizontally extends across the flap and which secures kitchen utensils, such as knives, in place. The flap can have multiple aligned pouches which extend laterally across a bottom portion of the flap. A distance above the flaps is a base fastener which extends on a base from one lateral end of the flap to the opposite lateral end. When the handles of kitchen utensils, such as knives, are placed inside respective pouches, the sharpened section of each knife is at least partially exposed. A strap fastener is attached at one lateral end of the flap and removably engages with the base fastener. The strap fastener secures the knives in place by overlapping with the knives and engaging with the exposed portions of the base fastener.

Abstract: This disclosure is directed to systems, methods, and devices related to providing the execution of multi-operation sequences based on a trigger occurring which may be a voice-controlled utterance or execution may be based on a trigger occurring and a condition occurring. In accordance with various principles disclosed herein, multi-operation sequences may be executed based on voice-controlled commands and the identification that a trigger has occurred. The voice-controlled electronic devices can be configured to communicate with, and to directly control the operation of, a wide array of other devices. These devices can include, without limitation, outlets that can be turned ON and OFF remotely such that anything plugged into them can be controlled, turning lights ON and OFF, setting the temperature of a network accessible thermostat, etc.

Abstract: An illustrative volumetric capture system accesses a machine learning model associated with bodies of a particular body type, as well as a two-dimensional (2D) image captured by a capture device located at a real-world scene. The 2D image depicts a body of the particular body type that is present at the real-world scene. Using the machine learning model and based on the 2D image, the volumetric capture system identifies a 2D joint location, from a perspective of the capture device, of a particular joint of the body. The volumetric capture system also generates a three-dimensional (3D) reference model of the body that represents the particular joint of the body at a 3D joint location that is determined based on the 2D joint location identified using the machine learning model. Corresponding methods and systems are also disclosed.

Abstract: In one embodiment, a removable pneumatic connector, comprises a body having a plurality of bores passing through, each bore surrounded by a sealing member on an inner surface of the body. A plurality of gas lines may be placed within a corresponding bore. A vacuum port is disposed on the inner surface of the body, and an outer seal on the inner surface of the body surrounds the sealing members and the vacuum port. A vacuum line may be placed within the vacuum port, and configured to deliver negative pressure to the vacuum port. A vacuum holding area is created in the volume between the outer seal and each of the sealing members when the inner surface of the body is placed against a substrate. When the vacuum line is activated, a vacuum is created within the vacuum holding area, creating a positive seal between the body and the substrate.