Abstract: Techniques for performing image alignment between a first image generated by a first camera and a second image generated by a second camera are disclosed. The image alignment is performed using staggered feature extraction in which a set of features are reused to align the second image with the first image. A first set of features are identified from within the first image, and a second set of features, which were previously detected within the second image, are accessed. The second set of features were previously used at least once to perform a previous image alignment operation. A current image alignment operation is performed by using the first set of features and by reusing the second set of features to align the first image with the second image.

Abstract: Techniques for generating an aggregated set of features from multiple images generated by a camera are disclosed. A first image generated by the camera is accessed, where the first image was generated at a first time. A first set of features are identified from within the first image. A second image generated by the camera is accessed, where the second image is generated at a subsequent, second time. A second set of features are identified from within the second image. Movement data is obtained. This movement data details a movement of the camera between the first and second times. The movement data is used to reproject a pose embodied in the first image to correspond to a pose embodied in the second image. The embodiments aggregate the two sets of features to generate the aggregated set of features. The aggregated set of features for the camera are then cached.

Abstract: A system for generating high dynamic range (HDR) imagery obtains a history frame associated with a first timepoint and with a first set of image capture parameters. The system obtains an image frame associated with a particular set of image capture parameters and captured at a second timepoint that is subsequent to the first timepoint, and the particular set of image capture parameters comprises the first set of image capture parameters or a second set of image capture parameters. The system generates a normalized image frame by applying a normalization operation to the image frame. The normalization operation may be determined based upon the particular set of image capture parameters. The system generates a weight map based upon at least the normalized image frame and generate an HDR image frame based upon the normalized image frame, the history frame, and the weight map.

Abstract: Techniques to temporally filter images via a filtering weight computation are disclosed. A first image having a first timestamp and a second image having a second timestamp are acquired. These images are generated by a camera, and the first timestamp is before the second timestamp. A motion compensation (MC) operation is performed on the first image to produce an MC image. A difference image is generated using the MC image and the second image. The difference image reflects differences in intensities that exist between the two images. A local weight map is generated based on those differences. A global weight map is generated based on certain IMU data. A final weight map is generated based on a combination of the local weight map and the global weight map. The final weight map is used to generate a filtered image.

Abstract: Techniques to temporally filter images via a filtering weight computation are disclosed. A first image having a first timestamp and a second image having a second timestamp are acquired. These images are generated by a camera, and the first timestamp is before the second timestamp. A motion compensation (MC) operation is performed on the first image to produce an MC image. A difference image is generated using the MC image and the second image. The difference image reflects differences in intensities that exist between the two images. A local weight map is generated based on those differences. A global weight map is generated based on certain IMU data. A final weight map is generated based on a combination of the local weight map and the global weight map. The final weight map is used to generate a filtered image.

Abstract: A system for generating high dynamic range (HDR) imagery obtains a history frame associated with a first timepoint and with a first set of image capture parameters. The system obtains an image frame associated with a particular set of image capture parameters and captured at a second timepoint that is subsequent to the first timepoint, and the particular set of image capture parameters comprises the first set of image capture parameters or a second set of image capture parameters. The system generates a normalized image frame by applying a normalization operation to the image frame. The normalization operation may be determined based upon the particular set of image capture parameters. The system generates a weight map based upon at least the normalized image frame and generate an HDR image frame based upon the normalized image frame, the history frame, and the weight map.

Abstract: Techniques to temporally filter images via a filtering weight computation are disclosed. A first image having a first timestamp and a second image having a second timestamp are acquired. These images are generated by a camera, and the first timestamp is before the second timestamp. A motion compensation (MC) operation is performed on the first image to produce an MC image. A difference image is generated using the MC image and the second image. The difference image reflects differences in intensities that exist between the two images. A local weight map is generated based on those differences. A global weight map is generated based on certain IMU data. A final weight map is generated based on a combination of the local weight map and the global weight map. The final weight map is used to generate a filtered image.

Abstract: Systems are configured for generating temporally corrected pass-through images. In some instances, the systems obtain depth maps of an environment at a first timepoint, generate a 3D representation of the environment by unprojecting the depth information represented in the depth map, and obtain one or more first images of the environment captured at a second timepoint. The systems may also be configured to perform a first intermediate projection to identify first texture information from the one or more first images, identify a display pose associated with the system, generate a display projection of the 3D representation, and creating a composite image based on the display projection and the first texture information.

Abstract: Systems are configured for generating temporally corrected pass-through images. In some instances, the systems obtain depth maps of an environment at a first timepoint, generate a 3D representation of the environment by unprojecting the depth information represented in the depth map, and obtain one or more first images of the environment captured at a second timepoint. The systems may also be configured to perform a first intermediate projection to identify first texture information from the one or more first images, identify a display pose associated with the system, generate a display projection of the 3D representation, and creating a composite image based on the display projection and the first texture information.