Kabushiki Kaisha Toshiba

Honda Motor Co., Ltd.

Toshiba Electronic Devices & Storage Corporation

Tokyo Institute of Technology

Akihito Seki

Ryo Nakashima

Nao Mishima

Masaki Yamazaki

Satoshi Ito

Masako Kashiwagi

Yuta Itoh

Kenichi Shimoyama

Norihiro Nakamura

Tomoki Watanabe

Ryuzo Okada

Yusuke Tazoe

Masatoshi Okutomi

Yuma Sano

Takaaki Kuratate

Tomoya Tsuruyama

Shunsuke Sakurai

Hideaki Uchiyama

Aira Hotta

Takayuki Sugiura

Mayu Okumura

Takuya Miyamoto

Norihiko Nakamura

Naoki Nishizawa

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.

Image processing device, method, and storage medium

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.

Estimation device and method

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.

Learning method, storage medium, and image processing device

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.

Image processing device, ranging device, and method

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.

Information processing device, information processing method, and computer program product

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.

Estimation device, estimation method, and computer program product

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.

Information processing device, information processing method, computer program product, and vehicle control system

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.

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.

An estimation device according to an embodiment includes

Estimation device, movable body, estimation method, and computer program product

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.

Information generating device, vehicle control system, information generation method, and computer program product

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.

Ranging device, image processing device and method

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.

Position and attitude estimation device, position and attitude estimation method, and storage medium

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.

Learning device, estimating device, learning method, and computer program product

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.

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Kanagawa, Japan

Akihito Seki