Denso Corporation

Denso Wave Corporation

Denso It Laboratory, Inc.

Yusuke Sekikawa

Kunihiko Chiba

Koichiro Suzuki

Yousuke Hattori

Yang Shi

Takahiro Koguchi

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.

Peripheral video generation device, peripheral video generation method, and storage medium storing program

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.

Image restoration device, image restoration method, and image restoration program

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.

Object identification apparatus, moving body system, object identification method, object identification model learning method, and object identification model learning apparatus

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Tokyo, Japan

Yusuke Sekikawa