Abstract: A computer-implemented method of estimating a 6D pose and shape of one or more objects from a 2D image, comprises the steps of: detecting, within the 2D image, one or more 2D regions of interest, each 2D region of interest containing a corresponding object among the one of more objects; cropping out a corresponding pixel value array, coordinate tensor , and feature map for each 2D region of interest; concatenating the corresponding pixel value array, coordinate tensor, and feature map for each 2D region of interest; and inferring, for each 2D region of interest, a 4D quaternion describing a rotation of the corresponding object in the 3D rotation group, a 2D centroid, which is a projection of a 3D translation of the corresponding object onto a plane of the 2D image given a camera matrix associated to the 2D, image, a distance from a viewpoint of the 2D image to the corresponding object a size and a class-specific latent shape vector of the corresponding object.

Abstract: A system and a method for processing an image include inputting the image to a neural network configured to: obtain a plurality of feature maps, each feature map having a respective resolution and a respective depth, perform a classification on each feature map to deliver, for each feature map: the type of at least one object visible on the image, the position and shape in the image of at least one two-dimensional bounding box surrounding the at least one object, at least one possible viewpoint for the at least one object, at least one possible in-plane rotation for the at least one object.

Abstract: A system and a method for processing an image include inputting the image to a neural network configured to: obtain a plurality of feature maps, each feature map having a respective resolution and a respective depth, perform a classification on each feature map to deliver, for each feature map: the type of at least one object visible on the image, the position and shape in the image of at least one two-dimensional bounding box surrounding the at least one object, a plurality of rotation hypotheses for the at least one object.

Abstract: A system and a method for processing an image include inputting the image to a neural network configured to: obtain a plurality of feature maps, each feature map having a respective resolution and a respective depth, perform a classification on each feature map to deliver, for each feature map: the type of at least one object visible on the image, the position and shape in the image of at least one two-dimensional bounding box surrounding the at least one object, at least one possible viewpoint for the at least one object, at least one possible in-plane rotation for the at least one object.

Abstract: In one embodiment, example systems and methods relate to a manner of generating 3D representations from monocular 2D images. A monocular 2D image is captured by a camera. The 2D image is processed to create one or more feature maps. The features may include depth features, or object labels, for example. Based on the image and the feature map, regions-of-interest corresponding to vehicles in the image are determined. For each region-of-interest a lifting function is applied to the region-of-interest to determine values such as height and width, camera distance, and rotation. The determined values are used to create an eight-point box that is a 3D representation of the vehicle depicted by the region-of-interest. The 3D representation can be used for a variety of purposes such as route planning, object avoidance, or as training data, for example.

Abstract: In one embodiment, example systems and methods relate to a manner of generating 3D representations from monocular 2D images. A monocular 2D image is captured by a camera. The 2D image is processed to create one or more feature maps. The features may include depth features, or object labels, for example. Based on the image and the feature map, regions-of-interest corresponding to vehicles in the image are determined. For each region-of-interest a lifting function is applied to the region-of-interest to determine values such as height and width, camera distance, and rotation. The determined values are used to create an eight-point box that is a 3D representation of the vehicle depicted by the region-of-interest. The 3D representation can be used for a variety of purposes such as route planning, object avoidance, or as training data, for example.