Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive a plurality of first images of an environment in a first lighting condition, classify pixels of the first images into categories, mask the pixels belonging to at least one of the categories from the first images, generate a three-dimensional representation of the environment based on the masked first images, and generate a second image of the environment in a second lighting condition based on the three-dimensional representation and on a first one of the first images.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive a first image of a scene in a first lighting condition, generate a three-dimensional representation of the scene based on the first image, and generate a second image of the scene in a second lighting condition based on the three-dimensional representation and on the first image. The first image is an only image of the scene used for generating the three-dimensional representation. The first image is an only image of the scene used for generating the second image.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive a plurality of first images of an environment in a first lighting condition, classify pixels of the first images into categories, mask the pixels belonging to at least one of the categories from the first images, generate a three-dimensional representation of the environment based on the masked first images, and generate a second image of the environment in a second lighting condition based on the three-dimensional representation and on a first one of the first images.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive a first image of a scene in a first lighting condition, generate a three-dimensional representation of the scene based on the first image, and generate a second image of the scene in a second lighting condition based on the three-dimensional representation and on the first image. The first image is an only image of the scene used for generating the three-dimensional representation. The first image is an only image of the scene used for generating the second image.

Abstract: A system, including a processor and a memory, the memory including instructions to be executed by the processor train a deep neural network based on plurality of real-world images, determine the accuracy of the deep neural network is below a threshold based on identifying one or more physical features by the deep neural network, including one or more object types, in the plurality of real-world images and generate a plurality of synthetic images based on the accuracy of the deep neural network is below a threshold based on identifying the one or more physical features using a photo-realistic image rendering software program and a generative adversarial network. The instructions can include further instructions to retrain the deep neural network based on the plurality of real-world images and the plurality of synthetic images and output the retrained deep neural network.

Abstract: A system, including a processor and a memory, the memory including instructions to be executed by the processor to train a deep neural network based on a plurality of real-world images, determine the accuracy of the deep neural network is below a threshold based on identifying one or more physical features by the deep neural network, including one or more object types, in the plurality of real-world images and generate a plurality of synthetic images based on the accuracy of the deep neural network is below a threshold based on identifying the one or more physical features using a photo-realistic image rendering software program and a generative adversarial network. The instructions can include further instructions to retrain the deep neural network based on the plurality of real-world images and the plurality of synthetic images and output the retrained deep neural network.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to input an image to a first layer of a machine learning program, the first layer trained to identify one or more quadrilateral regions in the image, upon identifying the one or more quadrilateral regions, input the collected image to a second layer of a machine learning program, the second layer trained to identify a plurality of sets of vertices, each set of vertices defining a respective polygonal area, identify one of the polygonal areas in which to park a vehicle, and actuate one or more vehicle components to move the vehicle into the identified polygonal area.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to input an image to a first layer of a machine learning program, the first layer trained to identify one or more quadrilateral regions in the image, upon identifying the one or more quadrilateral regions, input the collected image to a second layer of a machine learning program, the second layer trained to identify a plurality of sets of vertices, each set of vertices defining a respective polygonal area, identify one of the polygonal areas in which to park a vehicle, and actuate one or more vehicle components to move the vehicle into the identified polygonal area.

Abstract: The present disclosure discloses a system and a method. The system and the method generate, via a deep neural network, a first synthetic image based on a simulated image, generate a segmentation mask based on the synthetic image, compare the segmentation mask with a ground truth mask of the synthetic image, update the deep neural network based on the comparison, and generate, via the updated deep neural network, a second synthetic image based on the simulated image.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to generate a synthetic image and corresponding ground truth and generate a plurality of domain adapted synthetic images by processing the synthetic image with a variational auto encoder-generative adversarial network (VAE-GAN), wherein the VAE-GAN is trained to adapt the synthetic image from a first domain to a second domain. The instructions can include further instructions to train a deep neural network (DNN) based on the domain adapted synthetic images and the corresponding ground truth and process images with the trained deep neural network to determine objects.

Abstract: The present disclosure discloses a system and a method. The system and the method generate, via a deep neural network, a first synthetic image based on a simulated image, generate a segmentation mask based on the synthetic image, compare the segmentation mask with a ground truth mask of the synthetic image, update the deep neural network based on the comparison, and generate, via the updated deep neural network, a second synthetic image based on the simulated image.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to generate a synthetic image and corresponding ground truth and generate a plurality of domain adapted synthetic images by processing the synthetic image with a variational auto encoder-generative adversarial network (VAE-GAN), wherein the VAE-GAN is trained to adapt the synthetic image from a first domain to a second domain. The instructions can include further instructions to train a deep neural network (DNN) based on the domain adapted synthetic images and the corresponding ground truth and process images with the trained deep neural network to determine objects.