Abstract: Examples disclosed herein may involve a computing system that is configured to (i) obtain previously-derived perception data for a collection of sensor data including a sequence of frames observed by a vehicle within one or more scenes, where the previously-derived perception data includes a respective set of object-level information for each of a plurality of objects detected within the sequence of frames, (ii) derive supplemental object-level information for at least one object detected within the sequence of frames that adds to the previously-derived object-level information for the at least one object, (iii) augment the previously-derived perception data to include the supplemental object-level information for the at least one object, and (iv) store the augmented perception data in an arrangement that encodes a hierarchical relationship between the plurality of objects, the sequence of frames, and the one or more scenes.

Abstract: Examples disclosed herein may involve a computing system that is configured to (i) obtain previously-derived perception data for a collection of sensor data including a sequence of frames observed by a vehicle within one or more scenes, where the previously-derived perception data includes a respective set of object-level information for each of a plurality of objects detected within the sequence of frames, (ii) derive supplemental object-level information for at least one object detected within the sequence of frames that adds to the previously-derived object-level information for the at least one object, (iii) augment the previously-derived perception data to include the supplemental object-level information for the at least one object, and (iv) store the augmented perception data in an arrangement that encodes a hierarchical relationship between the plurality of objects, the sequence of frames, and the one or more scenes.

Abstract: Examples disclosed herein may involve a computing system that is configured to (i) obtain previously-derived perception data for a collection of sensor data including a sequence of frames observed by a vehicle within one or more scenes, where the previously-derived perception data includes a respective set of object-level information for each of a plurality of objects detected within the sequence of frames, (ii) derive supplemental object-level information for at least one object detected within the sequence of frames that adds to the previously-derived object-level information for the at least one object, (iii) augment the previously-derived perception data to include the supplemental object-level information for the at least one object, and (iv) store the augmented perception data in an arrangement that encodes a hierarchical relationship between the plurality of objects, the sequence of frames, and the one or more scenes.

Abstract: Methods, systems, and devices are provided for calibrating a light ranging system and using the system to track environmental objects. In embodiments, the approach involves installing light ranging devices, such as lidar devices, on the vehicle exterior. The light ranging system may be calibrated using a calibration device to scan the vehicle exterior and construct a three-dimensional model of the vehicle exterior comprising the positions of the installed light ranging devices on the vehicle exterior. The calibrated light ranging system may use the model in conjunction with ranging data collected by the installed light ranging devices to track objects in the environment. In this way, the light ranging system may detect a proximity of environmental objects and help a driver of the vehicle avoid potential collisions. The light ranging system may further measure the vehicle exterior and thereby detect changes to the vehicle exterior.

Abstract: Methods, systems, and devices are provided for calibrating a light ranging system and using the system to track environmental objects. In embodiments, the approach involves installing light ranging devices, such as lidar devices, on the vehicle exterior. The light ranging system may be calibrated using a calibration device to scan the vehicle exterior and construct a three-dimensional model of the vehicle exterior comprising the positions of the installed light ranging devices on the vehicle exterior. The calibrated light ranging system may use the model in conjunction with ranging data collected by the installed light ranging devices to track objects in the environment. In this way, the light ranging system may detect a proximity of environmental objects and help a driver of the vehicle avoid potential collisions. The light ranging system may further measure the vehicle exterior and thereby detect changes to the vehicle exterior.

Abstract: A computer implemented method of determining a latent image from an observed image is disclosed. The method comprises implementing a plurality of image processing operations within a single optimization framework, wherein the single optimization framework comprises solving a linear minimization expression. The method further comprises mapping the linear minimization expression onto at least one non-linear solver. Further, the method comprises using the non-linear solver, iteratively solving the linear minimization expression in order to extract the latent image from the observed image, wherein the linear minimization expression comprises: a data term, and a regularization term, and wherein the regularization term comprises a plurality of non-linear image priors.

Abstract: A computer implemented method of determining a latent image from an observed image is disclosed. The method comprises implementing a plurality of image processing operations within a single optimization framework, wherein the single optimization framework comprises solving a linear minimization expression. The method further comprises mapping the linear minimization expression onto at least one non-linear solver. Further, the method comprises using the non-linear solver, iteratively solving the linear minimization expression in order to extract the latent image from the observed image, wherein the linear minimization expression comprises: a data term, and a regularization term, and wherein the regularization term comprises a plurality of non-linear image priors.

Abstract: A computer implemented method of determining a latent image from an observed image is disclosed. The method comprises implementing a plurality of image processing operations within a single optimization framework, wherein the single optimization framework comprises solving a linear minimization expression. The method further comprises mapping the linear minimization expression onto at least one non-linear solver. Further, the method comprises using the non-linear solver, iteratively solving the linear minimization expression in order to extract the latent image from the observed image, wherein the linear minimization expression comprises: a data term, and a regularization term, and wherein the regularization term comprises a plurality of non-linear image priors.

Abstract: There is provided a computer-implemented method for solving inverse imaging problems to compensate for distortions in an image. The method comprises: minimizing a cost objective function containing a data fitting term and one or more image prior terms to each of the plurality of channels, the one or more image prior terms comprising cross-channel information for a plurality of channels derived from the image.

Abstract: There is provided a computer-implemented method for solving inverse imaging problems to compensate for distortions in an image. The method comprises: minimizing a cost objective function containing a data fitting term and one or more image prior terms to each of the plurality of channels, the one or more image prior terms comprising cross-channel information for a plurality of channels derived from the image.

Abstract: A computer implemented method of determining a latent image from an observed image is disclosed. The method comprises implementing a plurality of image processing operations within a single optimization framework, wherein the single optimization framework comprises solving a linear minimization expression. The method further comprises mapping the linear minimization expression onto at least one non-linear solver. Further, the method comprises using the non-linear solver, iteratively solving the linear minimization expression in order to extract the latent image from the observed image, wherein the linear minimization expression comprises: a data term, and a regularization term, and wherein the regularization term comprises a plurality of non-linear image priors.

Abstract: Methods and apparatus may be applied to reconstruct pixel values in saturated regions of an image. Saturated regions are identified and hues for pixels in the saturated regions are estimated based on hues in boundaries of the saturated regions. Gradients for pixel values in saturated color channels within the saturated region may be estimated based on known gradients for non-saturated color channels. Reconstructed pixel values may be derived from the estimated gradients. The methods and apparatus may be applied in conjunction with dynamic range expansion.

Abstract: Methods and apparatus may be applied to reconstruct pixel values in saturated regions of an image. Saturated regions are identified and hues for pixels in the saturated regions are estimated based on hues in boundaries of the saturated regions. Gradients for pixel values in saturated color channels within the saturated region may be estimated based on known gradients for non-saturated color channels. Reconstructed pixel values may be derived from the estimated gradients. The methods and apparatus may be applied in conjunction with dynamic range expansion.