Abstract: Provided are an apparatus and method for generating a depth map by using a volumetric feature. The method may generate a single feature map for a base image included in a surround-view image by performing encoding and postprocessing on the base image, and generate a volumetric feature by encoding the single feature map with depth information and then projecting a result of the encoding into a three-dimensional space. In this method, a depth map of a surround-view image may be generated by using a depth decoder to decode a volumetric feature.

Abstract: Provided is a method of improving an object recognition rate including recognizing a first object in a first image obtained while driving, detecting whether the recognized first object has disappeared for a preset time period and then reappeared in the first image, based on detecting that the first object has reappeared, calculating training data for the first object, and controlling such that a recognition model for recognizing an object included in an image is to be trained based on information based on the calculated training data.

Abstract: Provided are a data processing method and apparatus for training a depth information estimation model. The data processing method for training a depth information estimation model, includes: logging image data collected from one or more cameras; transmitting the logged image data to a database; configuring training data, based on the image data of the database; and training a model, based on the training data.

Abstract: Disclosed is a stereo matching method for generating a disparity map from a stereo image. The stereo matching method may include obtaining a cost volume by applying a first convolutional neural network (CNN) and a second CNN to a left image and a right image, respectively, wherein the cost volume is determined based on feature maps extracted from the left image and the right image, respectively, performing normalization on the cost volume by applying a third CNN, up-sampling the normalized cost volume, and obtaining a disparity map by applying regression analysis to the up-sampled cost volume.

Abstract: Disclosed is a stereo matching method for generating a disparity map from a stereo image. The stereo matching method may include obtaining a cost volume by applying a first convolutional neural network (CNN) and a second CNN to a left image and a right image, respectively, wherein the cost volume is determined based on feature maps extracted from the left image and the right image, respectively, performing normalization on the cost volume by applying a third CNN, up-sampling the normalized cost volume, and obtaining a disparity map by applying regression analysis to the up-sampled cost volume.