Abstract: An apparatus and a method remove noise from a point cloud and generate a depth map of a monocular camera image. The apparatus includes a camera sensor that photographs a monocular camera image. The apparatus also includes a lidar sensor that generates the point cloud corresponding to the monocular camera image. The apparatus also includes a controller that removes noise from the point cloud and generates the depth map of the monocular camera image based on the point cloud from which the noise is removed.

Abstract: A method for camera-Lidar calibration includes acquiring an image captured by a camera at a specific time point and a Lidar point cloud captured by a Lidar and projected onto a camera coordinate system at the specific time point, extracting a ground edge image corresponding to an edge of a ground surface, from the image extracting a road mark point cloud representing a road mark on the ground surface, from the Lidar point cloud, generating a first variation indicating a predicted position variation by predicting a position variation of the road mark point cloud to the ground edge image, through a neural network, and calibrating the camera and the Lidar, based on the first variation.

Abstract: In a method and apparatus for generating a depth map, the method includes obtaining a LiDAR point cloud generated by a LiDAR, obtaining a 3D bounding box for at least a portion of the LiDAR point cloud, generating a first vehicle mask corresponding to the 3D bounding box from an image point cloud obtained by projecting coordinates of points of the LiDAR point cloud into an image coordinate system, and generating the depth map by removing distant points that are not included in the 3D bounding box among points included in the first vehicle mask.

Abstract: The present disclosure relates to an apparatus for training and causing autonomous driving control of a vehicle. The apparatus may comprise at least one processor, and a memory storing instructions, when executed by the at least one processor, cause the apparatus to obtain, based on a depth map obtained from a cluster of points at a target time point, a depth distribution map, obtain, based on an input image that is associated with the target time point and that is applied to a monocular depth estimation (MDE) model, a depth estimation map, update, based on a loss function group applied to the MDE model, a plurality of weights included in the MDE model, wherein the loss function group may comprise a first loss function that is obtained based on the depth distribution map and the depth estimation map, and output a signal indicating the updated plurality of weights.

Abstract: A learning device, a learning method thereof, a test device using the same, and a test method using the same are provided. The learning device may obtain a target image and a source image, generate an estimated depth map based on the target image via a first network, generate pose change information corresponding to a pose change between the target image and the source image, generate a composite image corresponding to the target image, determine a first loss based on the composite image and the target image, and determine a second loss, and back-propagate the first loss and the second loss and update a parameter of the first network and a parameter of the second network.

Abstract: A learning device is introduced. The device may comprise a processor, and memory storing instructions that, when executed by the processor, may cause the device to obtain at least one first depth map based on at least one piece of cloud data associated with surrounding environment information, and at least one first image associated with the at least one first depth map, determine, based on the at least one first depth map and the at least one first image, variance estimation information indicating a variance between the at least one first depth map and the at least one first image, back-propagate a variance loss based on the first variance estimation information, and variance ground truth (GT) information associated with the first variance estimation information, and update, based on the back-propagated variance loss, a parameter associated with determining the first variance estimation information.

Abstract: An apparatus for estimating a depth is introduced. The apparatus may comprise a camera configured to capture an image may comprise an object, and a processor configured to perform, based on a deep learning model, a deep learning process associated with the image, obtain, based on the deep learning process associated with the image, a first depth value, obtain a partial image by masking a partial region of the image, perform, based on the deep learning model, a deep learning process associated with the partial image, obtain, based on the deep learning process associated with the partial image, a second depth value, train the deep learning model to reduce a deviation between the first depth value and the second depth value, and estimate, based on the trained deep learning model, a depth of the object.

Abstract: A training data selection device for selecting training data and a training data selection method therefor are provided. The training data selection device includes a depth estimation network that applies depth estimation calculation to an input image obtained in real time to output depth distribution information corresponding to the input image. The device includes a vulnerability output device that outputs depth estimation vulnerability corresponding to the input image with reference to the depth distribution information. The device includes a training data acquisition support device that stores the input image and specific point cloud data corresponding to the input image as new training data in a certain storage space or transmits the input image and the specific point cloud data to another device, when it is determined that the depth estimation vulnerability is greater than or equal to a predetermined threshold.

Abstract: An apparatus and a method are for generating a depth map of a monocular camera image. The apparatus includes: a camera sensor that photographs a monocular camera image; a lidar sensor that generates a point cloud corresponding to the monocular camera image; and a controller that removes noise from the point cloud and generates the depth map of the monocular camera image based on the point cloud from which the noise is removed.