Abstract: According to one embodiment, an image processing device is used when measuring a capture distance from a capture device to a subject in an image using an image captured by the capture device and affected by aberration of an optical system of the capture device. The image processing device includes a processor. The processor is configured to acquire an image captured by the capture device, acquire configuration information relating to an optical system of the capture device, acquire a capture distance with respect to the image based on the acquired image, create a first capture condition of an image suitable for measuring a distance to the subject based on the acquired configuration information and the acquired capture distance, and output the created first capture condition.

Abstract: A measurement device of an embodiment includes an imaging unit, an acquisition unit, an output control unit, and an input unit. The imaging unit captures a first image. The acquisition unit acquires auxiliary information candidates for assisting in estimating the captured location of the first image based on the first image. The output control unit outputs the auxiliary information candidates to an output unit. The input unit receives input to select auxiliary information from the auxiliary information candidates.

Abstract: According to one embodiment, a ranging device includes storage and a processor. The storage is configured to store a statistical model generated by learning a bokeh that occurs in a first image affected by aberration of an optical system of a capture unit. The processor is configured to acquire a second image captured by the capture unit, acquire focus position information when the second image was captured, acquire a bokeh value indicating a bokeh of a subject in the second image, which is output from the statistical model by inputting the acquired second image to the statistical model, and convert the acquired bokeh value into a distance to the subject based on the acquired focus position information.

Abstract: According to one embodiment, an estimation device includes a processor. The processor is configured to acquire first and second images including a same subject continuously captured by a capture device installed in a mobile object, acquire a first distance to the subject included in the acquired first image and uncertainty of the first distance, extract a useful second distance from the acquired first distance based on the acquired uncertainty of the first distance, and estimate a position and orientation of the mobile object when the second image is captured by the capture device based on the acquired first and second images, and the extracted second distance.

Abstract: According to one embodiment, a learning method includes acquiring first multi-view images obtained by capturing a first subject and causing a statistical model to learn, based on first and second bokeh values output from the statistical model by inputting first and second images of the first multi-view images. The causing includes acquiring a first distance from the capture device to a first subject in the first image and a second distance from the capture device to a first subject in the second image, discriminating a relationship in length between the first and second distances, and causing the statistical model to learn such that a relationship in magnitude between the first and second bokeh values is equal to the discriminated relationship.

Abstract: A learning device includes one or more processors. The processors calculate a likelihood of belonging to a plurality of estimated classes, of learning data, by using an estimation model for estimating to which of the estimated classes input data belongs. The processors calculate a weight of a loss function to be used in learning the estimation model such that, when a likelihood of a first class that is closer to correct data than other estimated classes among the estimated classes and likelihoods of a second class and a third class that are adjacent to the first class are applied to a function having a predetermined shape, a position that has an extreme value of the function corresponds to the correct data. The processors learn the estimation model by using the loss function.

Abstract: According to one embodiment, an image processing device includes first storage and a processor. The first storage is configured to store a first statistical model generated by learning bokeh which occurs in a first image. The processor is configured to acquire a second image, acquire a bokeh value which indicates bokeh occurring in the second image and an uncertainty level which indicates a level of uncertainty for the bokeh value, generate a first bokeh map based on the bokeh value and uncertainty level, and acquire a second bokeh map obtained by interpolating a bokeh value with respect to the first bokeh map. The bokeh value and the uncertainty level are output from the first statistical model by inputting the second image into the first statistical model.

Abstract: According to one embodiment, an image processing device includes storage and a processor. The storage is configured to store a statistical model generated by learning bokeh that occurs in a first image affected by aberration of an optical system and varies non-linearly in accordance with a distance to a subject in the first image. The processor is configured to acquire a second image affected by the aberration of the optical system, perform color correction on the second image to reduce a number of colors expressed in the second image, and input a third image, obtained by performing the color correction on the second image, into the statistical model and acquire first distance information indicating a distance to a subject in the third image.

Abstract: According to an embodiment, an information processing device is configured to: estimate, based on target image data, target position data representing an image-capturing position at which the target image data is captured; estimate, based on the target image data, target depth data representing a distance to an object from the image-capturing position; register new image-capturing information including the target image data, the target position data, and the target depth data; display, on a display device, map data representing a map of an environment in which the image data is captured; and display at least one of image data and depth data included in designated image-capturing information that is designated from among a plurality of pieces of image-capturing information, in association with a pixel position in the map data, the pixel position corresponding to position data included in the designated image-capturing information.

Abstract: An estimation apparatus according to an embodiment of the present disclosure includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: acquire first point cloud data; generate, from the first point cloud data, second point cloud data in which an attention point and at least one observation point are combined, the attention point gaining attention as a target of attribute estimation; and estimate an attribute of the attention point by calculating, for each attribute, a belonging probability of belonging to the attribute by using the second point cloud data.

Abstract: According to an embodiment, an estimation device includes one or more processors configured to: generate, from first point cloud data, second point cloud data obtained by combining an attention point and observation points; estimate an attribute of the attention point by an attribute indicated by an estimation result label having a higher belonging probability among belonging probabilities output from an attribute estimation neural network; estimate reliability of the estimation result label by a reliability estimation neural network; and display, on a display device, first display information generated by performing rendering on an object including an attention point whose attribute is estimated by an attribute of the estimation result label whose reliability is higher than a first threshold, and generated by not performing rendering on an object including an attention point whose attribute is estimated by an attribute of the estimation result label whose reliability is the first threshold or less.

Abstract: An information generating device according to an embodiment includes one or more hardware processors. The one or more hardware processors: acquire environmental information indicating a state of environment around a vehicle; acquire abnormality information indicating an abnormality on a road, the abnormality having possibility of affecting traveling of the vehicle; acquire vehicle information including at least one of information on a traveling operation of the vehicle and information on specifications of the vehicle; and generate notification information indicating an operation to be performed by the vehicle against the abnormality on the road, the notification information being generated based on the environmental information, the abnormality information, and the vehicle information.

Abstract: According to one embodiment, a position and attitude estimation device includes a processor. The processor is configured to acquire time-series images continuously captured by a capture device installed on a mobile object, estimate first position and attitude of the mobile object based on the acquired time-series images, estimate a distance to a subject included in the acquired time-series images and correct the estimated first position and attitude to a second position and attitude based on an actual scale, based on the estimated distance.

Abstract: According to one embodiment, a moving object action registration apparatus includes the following units. The acquisition unit acquires at least one reference image including a first reference image including a marker. The landmark detection unit detects, as a landmark, a feature point indicating a characteristic portion in an environment based on the at least one reference image. The marker detection unit detects the marker based on the first reference image. The calculation unit calculates a relative position between the landmark and the marker. The attribute identification unit identifies an attribute of the marker associated with an action. The registration unit registers the relative position and action information being based on the attribute in a database.

Abstract: According to an embodiment, an information processing device includes one or more processors. The processors are configured to: acquire a plurality of pieces of detection information including detection results at two-dimensional positions different from each other acquired by detection of an object by one or more detection devices through a transmission body, the plurality of pieces of detection information including distortion due to the transmission body that exists between the detection devices and the object; detect a feature point from each of the plurality of pieces of detection information; and estimate, by minimizing an error between a three-dimensional position corresponding to the feature point and a detection position of the feature point corrected based on a distortion map expressing distortion at each of the two-dimensional positions, the distortion map, the three-dimensional position, and the detection position.

Abstract: According to an embodiment, an information processing device include: one or more processors. The processors input data based on input data including first input data belonging to a first domain and second input data belonging to a second domain different from the first domain, to a first model, and acquire first output data indicating an execution result of a first task with the first model. The processors input data based on the input data to a second model, and acquire second output data indicating an execution result of a second task with the second model. The processors convert the first output data into first conversion data expressed in a form of an execution result of the second task. The processors generate supervised data of the second model for the first input data, based on the first conversion data and the second output data.

Abstract: A position estimation device according to an embodiment of the present disclosure is provided for estimating a self-position of a moving object device provided with one or more sensors for observing information related to movement. The position estimation device includes one or more hardware processors configured to: estimate a state amount representing a state of each of the one or more sensors based on an observation value of the corresponding sensor; calculate a confidence degree representing a degree of confidence of the state amount of each of the one or more sensors; select one or more target sensors from among the one or more sensors based on the confidence degree of the state amount of each of the one or more sensors; and estimate the self-position based on the observation value and the state amount of each of the selected one or more target sensors.

Abstract: An estimation device according to an embodiment includes one or more hardware processors configured to function as a query-image-acquisition unit, a query-imaging-condition-acquisition unit, a reference-image-acquisition unit, a reference-imaging-condition-acquisition unit, a feature-amount calculation unit, and a self-position calculation unit. The query-imaging-condition-acquisition unit acquires an imaging condition of a query image. The reference-imaging-condition-acquisition unit acquires an imaging condition of a reference image. The feature-amount calculation unit calculates a query image's feature based on the reference imaging condition, and calculates a reference image's feature based on the query imaging condition.

Abstract: A location estimating apparatus according to an embodiment includes a degree-of-similarity calculator, a correspondence calculator, and a location calculator. The degree-of-similarity calculator calculates a degree of similarity between the input image and the reference image by an arithmetic operation that varies, in a non-discrete manner, the degree of similarity in accordance with a non-discrete variation of a first parameter. The correspondence calculator calculates correspondence between a pixel of the input image and a pixel of the reference image by an arithmetic operation that varies, in a non-discrete manner, the correspondence in accordance with a non-discrete variation of a second parameter. The location calculator calculates a first location indicating a position and/or an orientation of the first imaging device when the input image is captured by an arithmetic operation that varies, in a non-discrete manner, the first location in accordance with a non-discrete variation of the correspondence.

Abstract: According to an embodiment, an estimation device includes one or more hardware processors configured to function as an acquisition unit, a first estimation unit, a first calculation unit, a second estimation unit, a second calculation unit, and a determination unit. The acquisition unit acquires an image captured by a camera. The first estimation unit estimates first estimation information from the image. The first calculation unit calculates first reliability of the first estimation information. The second estimation unit estimates second estimation information. The second calculation unit calculates second reliability of the second estimation information. The determination unit decides at least the position or pose of the camera from the first and second estimation information, based on the first and second reliability, and outputs at least the decided position or pose as a determination result. The second estimation unit estimates the second estimation information based on a past determination result.