Abstract: An object model learning method includes: in an object identification model forming a convolutional neural network and a warp structure warping a feature map extracted in the convolutional neural network to a different coordinate system, preparing, in the warp structure, a warp parameter for relating a position in the different coordinate system to a position in a coordinate system before warp; and learning the warp parameter to input a capture image in which an object is captured to the object identification model and output a viewpoint conversion map in which the object is identified in the different coordinate system.

Abstract: The image restoration device has an initialization block that initializes the luminance value of each pixel coordinate to an intermediate value in the luminance array list that stores one of a pair of polarity values and intermediate values as the luminance value for each pixel coordinate. The image restoration device also has an update block that updates the initialized luminance array list according to the pixel coordinates and polarity values for each event, and an output block that outputs the luminance array list updated by the update block over the shooting period as a binary image. By the update performed in the update block, the luminance values of the firing coordinates where the event fired in the luminance array list are overwritten by the polarity values of the event. In addition, the update preserves the luminance values of the non-firing coordinates in the luminance array list, excluding the firing coordinates.

Abstract: A peripheral video generation device includes: a video input unit that inputs peripheral video data captured by a plurality of cameras; a video composition unit that composites the peripheral video data to generate a composite video as viewed from a predetermined viewpoint; a three-dimensional shape estimation unit that estimates a three-dimensional shape of a peripheral object based on the peripheral video data; a shielded area estimation unit that uses an estimation result of the three-dimensional shape to estimate a shielded area not visible from the predetermined viewpoint in the composite video; an inference unit that infers a video of the shielded area using deep learning; and a video superimposition unit that superimposes the video inferred by the inference unit on the shielded area in the composite video.

Abstract: An object model learning method includes: in an object identification model forming a convolutional neural network and a warp structure warping a feature map extracted in the convolutional neural network to a different coordinate system, preparing, in the warp structure, a warp parameter for relating a position in the different coordinate system to a position in a coordinate system before warp; and learning the warp parameter to input a capture image in which an object is captured to the object identification model and output a viewpoint conversion map in which the object is identified in the different coordinate system.

Abstract: In a vehicle system, a space area estimation method, or a space area estimation apparatus, an outside of a vehicle is captured, and an image is generated. An object causing a blind angle in the image is recognized. A depth of the recognized object is estimated. An inside of a blind angle area formed by the object is estimated.

Abstract: A communication control unit performs communications with an external device via a first wireless communication unit of which communication distance is short and which is capable of performing high-speed communications or a second wireless communication unit of which communication distance is long compared with the first wireless communication unit and which is capable of performing low-speed communications. A communication state judging unit judges a communication state of the communications with the external device via the first wireless communication unit or the second wireless communication unit. The communication control unit performs switching between the communications with the external device via the first wireless communication unit and the communications with the external device via the second wireless communication unit based on the communication state judged by the communication state judging unit and/or a transmission data amount to be transmitted to the external device.