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: 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: A post-processing apparatus includes storage that stores a multi-task learning model. The post-processing apparatus also includes a controller that may generate a first entropy image from an output image of a first deep neural network (DNN) within the multi-task learning model. The controller may also generate a second entropy image from an output image of a second DNN in the multi-task learning model. The controller may post-process the output image of the first DNN based on the first entropy image and the second entropy image.

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.

Abstract: An active learning device and an active learning method are provided. The active learning device includes an input device configured to receive an input image, and a controller configured to extract a first feature from the input image, extract a second feature from a segmentation image corresponding to the input image, and extract a third feature from a depth image corresponding to the input image. The active learning device is also configured to determine the input image as a training image based on cosine distances among the first feature, the second feature and the third feature, and labels the training image.

Abstract: Provided are an apparatus for collecting a training image of a deep learning model and a method for the same. The apparatus may include a camera sensor configured to capture an image of a surrounding of a vehicle and a controller configured to determine an entropy of each pixel in the image. The controller may also be configured to determine an entropy of each class in the image based on the entropy of each pixel in the image and determine whether to collect the image based on the entropy of each class.

Abstract: An apparatus for selecting a training image of a deep learning model and a method thereof are disclosed. The apparatus includes an input device and a controller. The input device receives a simulation image and information about an object in the simulation image from a simulation tool and receives a training image corresponding to the simulation image from an image conversion device. The controller detects a similarity between a structure of the object in the simulation image and a structure of an object in the training image and determines validity of the training image based on the detected similarity.

Abstract: An apparatus for processing a deep learning model includes a first memory, a second memory, and a controller. The controller is configured to, for each layer of the deep learning model, detect memory usage, a first processing time corresponding to the first memory being used, and a second processing time corresponding to the second memory being used, and determine an optimal memory for each layer of the deep learning model based on an objective function.

Abstract: An apparatus and method for detecting performance degradation of an autonomous vehicle include a storage configured to store a deep learning model used for image recognition in the autonomous vehicle and a controller configured to determine entropy of each pixel among a plurality of pixels in an output image of the deep learning model, determine an average value of the entropy of the plurality of pixels, and detect performance degradation of the deep learning model based on the average value of the entropy of the plurality of pixels.

Abstract: A device can provide an air state detection floating device capable of remaining in the air, the device comprising: a power supply unit; at least one memory for storing instructions; at least one processor; a communication unit for transmitting and receiving data; a driving unit for generating a driving force; a first sensing unit for detecting information on an air state of an enclosed space; and a second sensing unit for generating location information, wherein the instructions are executable by the processor so as to the processor to perform operations, and the operations comprise: an operation that generates a control signal for movement in the enclosed space on the basis of the data; and an operation that controls the driving unit based on the control signal.

Abstract: A device can provide an air state detection floating device capable of remaining in the air, the device comprising: a power supply unit; at least one memory for storing instructions; at least one processor; a communication unit for transmitting and receiving data; a driving unit for generating a driving force; a first sensing unit for detecting information on an air state of an enclosed space; and a second sensing unit for generating location information, wherein the instructions are executable by the processor so as to the processor to perform operations, and the operations comprise: an operation that generates a control signal for movement in the enclosed space on the basis of the data; and an operation that controls the driving unit based on the control signal.

Abstract: The present invention relates to a skin preparation composition for external use having excellent antiseptic ability without using chemical antiseptics. More particularly, the present invention relates to a skin preparation composition for external use, comprising: glyceryl undecylenate having excellent antiseptic ability; and one or more mixtures of ethylhexylglycerin, glyceryl caprylate, p-anisic acid and a citrus mixed extract, thus improving antiseptic ability through the increased effects of antiseptic abilities of those materials.

Abstract: The present invention relates to a skin preparation composition for external use having excellent antiseptic ability without using chemical antiseptics. More particularly, the present invention relates to a skin preparation composition for external use, comprising: glyceryl undecylenate having excellent antiseptic ability; and one or more mixtures of ethylhexylglycerin, glyceryl caprylate, p-anisic acid and a citrus mixed extract, thus improving antiseptic ability through the increased effects of antiseptic abilities of those materials.