Abstract: Energy is transmitted to a body lumen or passageway in the form of pulsed electric fields (PEFs) and in a manner which provides focal therapy. In some embodiments, PEFs are delivered through independent electrically active electrodes of an energy delivery body, typically in a monopolar fashion. Such delivery concentrates the electrical energy over a smaller surface area, resulting in stronger effects than delivery through an electrode extending circumferentially around the lumen or passageway. It also forces the electrical energy to be delivered in a staged regional approach, mitigating the effect of preferential current pathways through the surrounding tissue. Focal delivery of PEFs can provide increased tissue lethality by employing precise timing and sequencing of energy delivery to the electrodes.

Abstract: Techniques include introducing a neural generator configured to produce novel faces that can be rendered at free camera viewpoints (e.g., at any angle with respect to the camera) and relit under an arbitrary high dynamic range (HDR) light map. A neural implicit intrinsic field takes a randomly sampled latent vector as input and produces as output per-point albedo, volume density, and reflectance properties for any queried 3D location. These outputs are aggregated via a volumetric rendering to produce low resolution albedo, diffuse shading, specular shading, and neural feature maps. The low resolution maps are then upsampled to produce high resolution maps and input into a neural renderer to produce relit images.

Abstract: A head-mounted device (HMD) can be configured to determine a request for recognizing at least one content item included within content framed within a display of the HMD. The HMD can be configured to initiate a head-tracking process that maintains a coordinate system with respect to the content, and a pointer-tracking process that tracks a pointer that is visible together with the content within the display. The HMD can be configured to capture a first image of the content and a second image of the content, the second image including the pointer. The HMD can be configured to map a location of the pointer within the second image to a corresponding image location within the first image, using the coordinate system, and provide the at least one content item from the corresponding image location.

Abstract: A lighting stage includes a plurality of lights that project alternating spherical color gradient illumination patterns onto an object or human performer at a predetermined frequency. The lighting stage also includes a plurality of cameras that capture images of an object or human performer corresponding to the alternating spherical color gradient illumination patterns. The lighting stage also includes a plurality of depth sensors that capture depth maps of the object or human performer at the predetermined frequency. The lighting stage also includes (or is associated with) one or more processors that implement a machine learning algorithm to produce a three-dimensional (3D) model of the object or human performer. The 3D model includes relighting parameters used to relight the 3D model under different lighting conditions.

Abstract: This document describes techniques and systems that enable autocalibration for multiple cameras using near-infrared (NIR) illuminators. The techniques and systems include a user device that uses NIR images, including dot images and flood images, captured by the multiple cameras. The user device implements an autocalibration module that normalizes the contrast of each image. Then, the autocalibration module detects dot features in the dot images and detects flood features in the flood images. The autocalibration module uses the flood features to disambiguate the dot features in the dot images. Then, the autocalibration module uses the disambiguated dot features to determine new calibration parameters for recalibration of the multiple cameras. In some aspects, the autocalibration module uses the dot images, rather than the flood images, to detect the flood features for disambiguating the dot features.

Abstract: This document describes techniques and systems that enable autocalibration for multiple cameras using near-infrared (NIR) illuminators. The techniques and systems include a user device that uses NIR images, including dot images and flood images, captured by the multiple cameras. The user device implements an autocalibration module that normalizes the contrast of each image. Then, the autocalibration module detects dot features in the dot images and detects flood features in the flood images. The autocalibration module uses the flood features to disambiguate the dot features in the dot images. Then, the autocalibration module uses the disambiguated dot features to determine new calibration parameters for recalibration of the multiple cameras. In some aspects, the autocalibration module uses the dot images, rather than the flood images, to detect the flood features for disambiguating the dot features.

Abstract: An electronic device estimates a pose of a hand by volumetrically deforming a signed distance field using a skinned tetrahedral mesh to locate a local minimum of an energy function, wherein the local minimum corresponds to the hand pose. The electronic device identifies a pose of the hand by fitting an implicit surface model of a hand to the pixels of a depth image that correspond to the hand. The electronic device uses a skinned tetrahedral mesh to warp space from a base pose to a deformed pose to define an articulated signed distance field from which the hand tracking module derives candidate poses of the hand. The electronic device then minimizes an energy function based on the distance of each corresponding pixel to identify the candidate pose that most closely approximates the pose of the hand.

Abstract: An electronic device estimates a depth map of an environment based on stereo depth images captured by depth cameras having exposure times that are offset from each other in conjunction with illuminators pulsing illumination patterns into the environment. A processor of the electronic device matches small sections of the depth images from the cameras to each other and to corresponding patches of immediately preceding depth images (e.g., a spatio-temporal image patch “cube”). The processor computes a matching cost for each spatio-temporal image patch cube by converting each spatio-temporal image patch into binary codes and defining a cost function between two stereo image patches as the difference between the binary codes. The processor minimizes the matching cost to generate a disparity map, and optimizes the disparity map by rejecting outliers using a decision tree with learned pixel offsets and refining subpixels to generate a depth map of the environment.

Abstract: An electronic device estimates a pose of a face by fitting a generative face model mesh to a depth map based on vertices of the face model mesh that are estimated to be visible from the point of view of a depth camera. A face tracking module of the electronic device receives a depth image of a face from a depth camera and generates a depth map of the face based on the depth image. The face tracking module identifies a pose of the face by fitting a face model mesh to the pixels of a depth map that correspond to the vertices of the face model mesh that are estimated to be visible from the point of view of the depth camera.

Abstract: An electronic device estimates a pose of a hand by volumetrically deforming a signed distance field using a skinned tetrahedral mesh to locate a local minimum of an energy function, wherein the local minimum corresponds to the hand pose. The electronic device identifies a pose of the hand by fitting an implicit surface model of a hand to the pixels of a depth image that correspond to the hand. The electronic device uses a skinned tetrahedral mesh to warp space from a base pose to a deformed pose to define an articulated signed distance field from which the hand tracking module derives candidate poses of the hand. The electronic device then minimizes an energy function based on the distance of each corresponding pixel to identify the candidate pose that most closely approximates the pose of the hand.

Abstract: An electronic device estimates a pose of a hand by volumetrically deforming a signed distance field using a skinned tetrahedral mesh to locate a local minimum of an energy function, wherein the local minimum corresponds to the hand pose. The electronic device identifies a pose of the hand by fitting an implicit surface model of a hand to the pixels of a depth image that correspond to the hand. The electronic device uses a skinned tetrahedral mesh to warp space from a base pose to a deformed pose to define an articulated signed distance field from which the hand tracking module derives candidate poses of the hand. The electronic device then minimizes an energy function based on the distance of each corresponding pixel to identify the candidate pose that most closely approximates the pose of the hand.

Abstract: A computing device has an input configured to receive data captured by at least one capture device where the data depicts at least part of an object moving in an environment. The computing device has a tracker configured to track a real-world position and orientation of the object using the captured data. A processor at the computing device is configured to compute and output feedback about performance of the tracker, where the feedback encourages a user to adjust movement of the object for improved tracking of the object by the tracker.

Abstract: A tracker is described which comprises an input configured to receive captured sensor data depicting an object. The tracker has a processor configured to access a rigged, smooth-surface model of the object and to compute values of pose parameters of the model by calculating an optimization to fit the model to data related to the captured sensor data. Variables representing correspondences between the data and the model are included in the optimization jointly with the pose parameters.

Abstract: An electronic device estimates a pose of a face by fitting a generative face model mesh to a depth map based on vertices of the face model mesh that are estimated to be visible from the point of view of a depth camera. A face tracking module of the electronic device receives a depth image of a face from a depth camera and generates a depth map of the face based on the depth image. The face tracking module identifies a pose of the face by fitting a face model mesh to the pixels of a depth map that correspond to the vertices of the face model mesh that are estimated to be visible from the point of view of the depth camera.

Abstract: An electronic device estimates a depth map of an environment based on stereo depth images captured by depth cameras having exposure times that are offset from each other in conjunction with illuminators pulsing illumination patterns into the environment. A processor of the electronic device matches small sections of the depth images from the cameras to each other and to corresponding patches of immediately preceding depth images (e.g., a spatio-temporal image patch “cube”). The processor computes a matching cost for each spatio-temporal image patch cube by converting each spatio-temporal image patch into binary codes and defining a cost function between two stereo image patches as the difference between the binary codes. The processor minimizes the matching cost to generate a disparity map, and optimizes the disparity map by rejecting outliers using a decision tree with learned pixel offsets and refining subpixels to generate a depth map of the environment.

Abstract: An electronic device estimates a pose of a hand by volumetrically deforming a signed distance field using a skinned tetrahedral mesh to locate a local minimum of an energy function, wherein the local minimum corresponds to the hand pose. The electronic device identifies a pose of the hand by fitting an implicit surface model of a hand to the pixels of a depth image that correspond to the hand. The electronic device uses a skinned tetrahedral mesh to warp space from a base pose to a deformed pose to define an articulated signed distance field from which the hand tracking module derives candidate poses of the hand. The electronic device then minimizes an energy function based on the distance of each corresponding pixel to identify the candidate pose that most closely approximates the pose of the hand.

Abstract: Model fitting from raw time of flight image data is described, for example, to track position and orientation of a human hand or other entity. In various examples, raw image data depicting the entity is received from a time of flight camera. A 3D model of the entity is accessed and used to render, from the 3D model, simulations of raw time of flight image data depicting the entity in a specified pose/shape. The simulated raw image data and at least part of the received raw image data are compared and on the basis of the comparison, parameters of the entity are computed.

Abstract: Examples describe an apparatus for calibrating a three dimensional (3D) mesh model of an articulated object. The articulated object is an instance of a specified object class. The apparatus comprises an input configured to receive captured sensor data depicting the object. The apparatus has a calibration engine configured to compute values of shape parameters of the 3D mesh model which indicate which member of the object class is depicted in the captured sensor data, in order to calibrate the 3D mesh model. The calibration engine is configured to compute the values of the shape parameters with an optimization process to find at least one potential local or global minimum of an energy function, the energy function expressing a degree of similarity between data rendered from the model and the received sensor data.

Abstract: A computing device has an input configured to receive data captured by at least one capture device where the data depicts at least part of an object moving in an environment. The computing device has a tracker configured to track a real-world position and orientation of the object using the captured data. A processor at the computing device is configured to compute and output feedback about performance of the tracker, where the feedback encourages a user to adjust movement of the object for improved tracking of the object by the tracker.

Abstract: A user interface comprises a display controller configured to render graphical data on a display, and a memory configured to receive captured sensor data depicting at least one hand of a user operating the user interface without touching the user interface. A tracker is configured to compute, from the captured sensor data, values of pose parameters of a three dimensional model of the hand, the pose parameters comprising position and orientation of each of a plurality of joints of the hand. A processor is configured to compute at least one position on the display from the pose parameters and to update the graphical data on the basis of the position.