Abstract: A depth based scanning system can be configured to determine whether pixel depth values of a depth map are within a depth range; determine a component of the depth map comprised of connected pixels each with a depth value within the depth range; replace the depth values of any pixels of the depth map that are not connected pixels; determine whether each pixel of the connected pixels of the component has at least a threshold number of neighboring pixels that have a depth value within the depth range; and for each pixel of the connected pixels of the component, if the pixel is determined to have at least the threshold number of neighboring pixels, replace its depth value with a filtered depth value that is based on the depth values of the neighboring pixels that have a depth value within the depth range.

Abstract: Techniques and systems are provided for performing object verification using radar images. For example, a first radar image and a second radar image are obtained, and features are extracted from the first radar image and the second radar image. A similarity is determined between an object represented by the first radar image and an object represented by the second radar image based on the features extracted from the first radar image and the features extracted from the second radar image. A determined similarity between these two sets of features is used to determine whether the object represented by the first radar image matches the object represented by the second radar image. Distances between the features in the two radar images can optionally also be compared and used to determine object similarity. The objects in the radar images may optionally be faces.

Abstract: Embodiments described herein can address these and other issues by using radar machine learning to address the radio frequency (RF) to perform object identification, including facial recognition. In particular, embodiments may obtain IQ samples by transmitting and receiving a plurality of data packets with a respective plurality of transmitter antenna elements and receiver antenna elements. I/Q samples indicative of a channel impulse responses of an identification region obtained from the transmission and reception of the plurality of data packets may then be used to identify, with an autoencoder, a physical object in the identification region.

Abstract: Embodiments described herein can address these and other issues by using radar machine learning to address the radio frequency (RF) to perform object identification, including facial recognition. In particular, embodiments may obtain IQ samples by transmitting and receiving a plurality of data packets with a respective plurality of transmitter antenna elements and receiver antenna elements, where each data packet of the plurality of data packets comprises one or more complementary pairs of Golay sequences. I/Q samples indicative of a channel impulse responses of an identification region obtained from the transmission and reception of the plurality of data packets may then be used to identify, with a random forest model, a physical object in the identification region.

Abstract: A method for determining a pixel value of a texture pixel associated with a three-dimensional scan of an object includes prioritizing a sequence of image frames in a queue based on one or more prioritization parameters. The method also includes selecting a first image frame from the queue. The method also includes determining a pixel value of the particular texture pixel in the first image frame. The method further includes selecting a second image frame from the queue. The second image frame has a higher priority than the first image frame based on the one or more prioritization parameters. The method also includes modifying the pixel value of the particular texture pixel based on a pixel value of the particular texture pixel in the second image frame to generate a modified pixel value of the particular texture pixel.

Abstract: A depth based scanning system can be configured to determine whether pixel depth values of a depth map are within a depth range; determine a component of the depth map comprised of connected pixels each with a depth value within the depth range; replace the depth values of any pixels of the depth map that are not connected pixels; determine whether each pixel of the connected pixels of the component has at least a threshold number of neighboring pixels that have a depth value within the depth range; and for each pixel of the connected pixels of the component, if the pixel is determined to have at least the threshold number of neighboring pixels, replace its depth value with a filtered depth value that is based on the depth values of the neighboring pixels that have a depth value within the depth range.

Abstract: A method performed by an electronic device is described. The method includes incrementally adding a current node to a graph. The method also includes incrementally determining a respective adaptive edge threshold for each candidate edge between the current node and one or more candidate neighbor nodes. The method further includes determining whether to accept or reject each candidate edge based on each respective adaptive edge threshold. The method additionally includes performing refining based on the graph to produce refined data. The method also includes producing a three-dimensional (3D) model based on the refined data.

Abstract: Methods and apparatuses are disclosed for assisting a user in performing a three dimensional scan of an object. An example user device to assist with scanning may include a processor. The user device may further include a scanner coupled to the processor and configured to perform a three dimensional scan of an object. The user device may also include a display to display a graphical user interface, wherein the display is coupled to the processor. The user device may further include a memory coupled to the processor and the display, the memory including one or more instructions that when executed by the processor cause the graphical user interface to display a target marker for a three dimensional (3D) scan and display a scanner position marker to assist in moving the scanner to a preferred location and direction.

Abstract: A method for texture reconstruction associated with a three-dimensional scan of an object includes scanning, at a processor, a sequence of image frames captured by an image capture device at different three-dimensional viewpoints. The method also includes generating a composite confidence map based on the sequence of image frames. The composite confidence map includes pixel values for scanned pixels in the sequence of image frames. The method further includes identifying one or more holes of a three-dimensional model based on the composite confidence map.

Abstract: A method is described. The method includes determining normalized radiance of an image sequence based on a camera response function (CRF). The method also includes determining one or more reliability images of the image sequence based on a reliability function corresponding to the CRF. The method further includes extracting features based on the normalized radiance of the image sequence. The method additionally includes optimizing a model based on the extracted features and the reliability images.

Abstract: A method performed by an electronic device is described. The method includes obtaining sensor data corresponding to multiple occupants from an interior of a vehicle. The method also includes obtaining, by a processor, at least one occupant status for at least one of the occupants based on a first portion of the sensor data. The method further includes identifying, by the processor, at least one vehicle operation in response to the at least one occupant status. The method additionally includes determining, by the processor, based at least on a second portion of the sensor data, whether to perform the at least one vehicle operation. The method also includes performing the at least one vehicle operation in a case that it is determined to perform the at least one vehicle operation.

Abstract: An apparatus includes a processor and a memory. The processor is configured to execute instructions stored at the memory to receive first characterization data and second characterization data. The first characterization data includes first values in a first order and corresponding to a first object. The second characterization data includes second values in a second order and corresponding to a second object. The processor is further configured to generate third characterization data and to generate fourth characterization. The third characterization data includes the first values in a third order. The fourth characterization data includes the second values in a fourth order. The processor is also configured to perform a first similarity operation using the first, second, third, and fourth characterization data to generate first result data and to determine whether the first object and the second object match based on the first result data.

Abstract: Methods and apparatuses are disclosed for assisting a user in performing a three dimensional scan of an object. An example user device to assist with scanning may include a processor. The user device may further include a scanner coupled to the processor and configured to perform a three dimensional scan of an object. The user device may also include a display to display a graphical user interface, wherein the display is coupled to the processor. The user device may further include a memory coupled to the processor and the display, the memory including one or more instructions that when executed by the processor cause the graphical user interface to display a target marker for a three dimensional (3D) scan and display a scanner position marker to assist in moving the scanner to a preferred location and direction.

Abstract: A method for adjusting pixel colors between image frames includes scanning, at a processor, a first image frame of a sequence of image frames. The method also includes determining a grayscale threshold based on characteristics of the first image frame to identify gray pixel candidates in the first image frame. The method further includes adjusting a pixel value of each pixel in the first image frame based on a chromatic adaptation transform estimation. The chromatic adaptation transform estimation is based on the gray pixel candidates. The grayscale threshold may be computed for each image frame in the sequence of image frames.

Abstract: A method for tracking an object by an electronic device is described. The method includes detecting an object position in an initial frame to produce a detected object position. The method also includes measuring one or more landmark positions based on the detected object position or a predicted object position. The method further includes predicting the object position in a subsequent frame based on the one or more landmark positions. The method additionally includes determining whether object tracking is lost. The method also includes avoiding performing object detection for the subsequent frame in a case that object tracking is maintained.

Abstract: A method includes receiving, from an image capture device, a first image frame of a sequence of image frames. The method also includes estimating, at a processor, a camera pose corresponding to the first image frame by comparing the first image frame to a second image frame. The second image frame precedes the first image frame in the sequence of image frames. The method further includes estimating, at the processor, a refined camera pose corresponding to the first image frame by comparing the first image frame to a keyframe. The keyframe corresponds to a particular image frame that precedes the second image frame in the sequence of image frames.

Abstract: A method includes generating, at an electronic device, a three-dimensional model of an object based on a sequence of images captured by an image capture device associated with the electronic device. The method further includes displaying the three-dimensional model via a display device associated with the electronic device. The method also includes, based on detecting that the three-dimensional model includes an anomaly, presenting, via the display device, one or more selectable options to enable correction of the anomaly.

Abstract: A method for determining a pixel value of a texture pixel associated with a three-dimensional scan of an object includes prioritizing a sequence of image frames in a queue based on one or more prioritization parameters. The method also includes selecting a first image frame from the queue. The method also includes determining a pixel value of the particular texture pixel in the first image frame. The method further includes selecting a second image frame from the queue. The second image frame has a higher priority than the first image frame based on the one or more prioritization parameters. The method also includes modifying the pixel value of the particular texture pixel based on a pixel value of the particular texture pixel in the second image frame to generate a modified pixel value of the particular texture pixel.

Abstract: A method for texture reconstruction associated with a three-dimensional scan of an object includes scanning, at a processor, a sequence of image frames captured by an image capture device at different three-dimensional viewpoints. The method also includes generating a composite confidence map based on the sequence of image frames. The composite confidence map includes pixel values for scanned pixels in the sequence of image frames. The method further includes identifying one or more holes of a three-dimensional model based on the composite confidence map.

Abstract: A method for adjusting pixel colors between image frames includes scanning, at a processor, a first image frame of a sequence of image frames. The method also includes determining a grayscale threshold based on characteristics of the first image frame to identify gray pixel candidates in the first image frame. The method further includes adjusting a pixel value of each pixel in the first image frame based on a chromatic adaptation transform estimation. The chromatic adaptation transform estimation is based on the gray pixel candidates. The grayscale threshold may be computed for each image frame in the sequence of image frames.