In one embodiment, a method includes accessing first image data generated by a first image sensor having a first filter array that has a first filter pattern. The first filter pattern includes a number of first filter types. The method also includes accessing second image data generated by a second image sensor having a second filter array that has a second filter pattern different from the first filter pattern. The second filter pattern includes a number of second filter types, the number of second filter types and the number of first filter types have at least one filter type in common. The method also includes determining a correspondence between one or more first pixels of the first image data and one or more second pixels of the second image data based on a portion of the first image data associated with the filter type in common.

In one embodiment, a method includes accessing first image data generated by a first image sensor having a first filter array that has a first filter pattern. The first filter pattern includes a number of first filter types. The method also includes accessing second image data generated by a second image sensor having a second filter array that has a second filter pattern different from the first filter pattern. The second filter pattern includes a number of second filter types, the number of second filter types and the number of first filter types have at least one filter type in common. The method also includes determining a correspondence between one or more first pixels of the first image data and one or more second pixels of the second image data based on a portion of the first image data associated with the filter type in common.

A solution to the problem of image and video stitching is disclosed that compensates for the effects of lens distortion, camera misalignment, and parallax in combining multiple images. The disclosed image stitching technique includes depth or disparity estimation, alignment, and blending processes configured to be computationally efficient and produce quality results by limiting the presence of noticeable seams and artifacts in the final stitched image. An inter-frame approach applies image stitching to video frames to maintain temporal continuity between successive frames across a stitched video output having a 360-degree viewing perspective. A temporal adjustment is configured to improve temporal continuity between a subsequent frame and a previous frame in a sequence of video frames.

A solution to the problem of image and video stitching is disclosed that compensates for the effects of lens distortion, camera misalignment, and parallax in combining multiple images. The disclosed image stitching technique includes depth or disparity estimation, alignment, and blending processes configured to be computationally efficient and produce quality results by limiting the presence of noticeable seams and artifacts in the final stitched image. An inter-frame approach applies image stitching to video frames to maintain temporal continuity between successive frames across a stitched video output having a 360-degree viewing perspective. A temporal adjustment is configured to improve temporal continuity between a subsequent frame and a previous frame in a sequence of video frames.

Systems, apparatuses, methods, and computer program products are disclosed for pricing a callable instrument. A plurality of corresponding pairs of Brownian motion paths and index value paths are determined corresponding to a set of dates. A deep neural network (DNN) of a backward DNN solver is trained until a convergence requirement is satisfied by for each pair of corresponding Brownian motion path and index value path, using the backward DNN solver to determine by iterating in reverse time order from a final discounted option payoff to an initial value. A statistical measure of spread of a set of initial values is determined and parameters of the DNN are modified based on the statistical measures of spread. Pricing information is determined by the backward DNN solver and provided such that a representation thereof is provided via an interactive user interface of a user computing device.

Systems, apparatuses, methods, and computer program products are disclosed for pricing a callable instrument. A plurality of corresponding pairs of Brownian motion paths and index value paths are determined corresponding to a set of dates. A deep neural network (DNN) of a backward DNN solver is trained until a convergence requirement is satisfied by for each pair of corresponding Brownian motion path and index value path, using the backward DNN solver to determine by iterating in reverse time order from a final discounted option payoff to an initial value. A statistical measure of spread of a set of initial values is determined and parameters of the DNN are modified based on the statistical measures of spread. Pricing information is determined by the backward DNN solver and provided such that a representation thereof is provided via an interactive user interface of a user computing device.

Embodiments of the present disclosure are directed to methods and systems for generating three-dimensional (3D) models and facial hair models representative of subjects (e.g., actors or actresses) using facial scanning technology. Methods accord to embodiments may be useful for performing facial capture on subjects with dense facial hair. Initial subject facial data, including facial frames and facial performance frames (e.g., images of the subject collected from a capture system) can be used to accurately predict the structure of the subject's face underneath their facial hair to produce a reference 3D facial shape of the subject. Likewise, image processing techniques can be used to identify facial hairs and generate a reference facial hair model. The reference 3D facial shape and reference facial hair mode can subsequently be used to generate performance 3D facial shapes and a performance facial hair model corresponding to a performance by the subject (e.g., reciting dialog).

Embodiments of the present disclosure are directed to methods and systems for generating three-dimensional (3D) models and facial hair models representative of subjects (e.g., actors or actresses) using facial scanning technology. Methods accord to embodiments may be useful for performing facial capture on subjects with dense facial hair. Initial subject facial data, including facial frames and facial performance frames (e.g., images of the subject collected from a capture system) can be used to accurately predict the structure of the subject's face underneath their facial hair to produce a reference 3D facial shape of the subject. Likewise, image processing techniques can be used to identify facial hairs and generate a reference facial hair model. The reference 3D facial shape and reference facial hair mode can subsequently be used to generate performance 3D facial shapes and a performance facial hair model corresponding to a performance by the subject (e.g., reciting dialog).

Embodiments of localization and mapping using depth modeling are described herein. In one example, frames of image data captured by sensor(s) from various poses within an environment are received over an interface. Keypoints are detected in the current frame, and matching keypoints are found in preceding frames. The pose of the current frame is determined based at least partially on depth models associated with the matching keypoints.

Embodiments of localization and mapping using depth modeling are described herein. In one example, frames of image data captured by sensor(s) from various poses within an environment are received over an interface. Keypoints are detected in the current frame, and matching keypoints are found in preceding frames. The pose of the current frame is determined based at least partially on depth models associated with the matching keypoints.