Masaaki Iiyama

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Recognizing structure of human body is important for modeling human motion. Human body is usually represented as an articulate model, which consists of the rigid parts and the joint points between them. The structure of the human body is specified by the joint points. In this paper we propose a novel method for estimating the location of joint points from(More)
We propose a method for acquiring a 3D shape of human body segments accurately. Using a light stripe triangulation range finder, we can acquire accurate the 3D shape of a motionless object in a dozen of seconds. If the object were to move during the scanning, the acquired shape would be distorted. Naturally, humans move slightly for making balance while(More)
In this paper, we propose a novel method for reconstructing the shape model of a non-rigid object. We represent the non-rigid object as the union of rigid components, and acquire range images of the object and motion of each component while the object varies its shape. We acquire the range images using one-shot scanning, and we use marker-based motion(More)
—This paper presents an artifact-free super-resolution texture mapping from multiple-view images. The multiple-view images are upscaled with a learning-based super-resolution technique and are mapped onto a 3D mesh model. However, mapping multiple-view images onto a 3D model is not an easy task, because artifacts may appear when different upscaled images(More)
In this paper, we propose an extension of light stripe trian-gulation for multiple of moving rigid objects. With traditional light stripe triangulation, the acquired shape of moving object would be distorted. If the subject is a rigid object, we can correct the distortion in the acquired shape based on its motion. However, when the subject consists of(More)
In this paper, we present a novel approach for extracting silhouettes by using a particular pattern that we call the random pattern. The volume intersection method reconstructs the shapes of 3D objects from their silhouettes obtained with multiple cameras. With the method, if some parts of the silhouettes are missed, the corresponding parts of the(More)
In this article, we discuss 3D shape reconstruction of an object in a rigid motion with the volume intersection method. When the object moves rigidly, the cameras change their relative positions to the object at every moment. To estimate the motion correctly, we propose new feature points called out-crop points on the reconstructed 3D shape. These points(More)
—Most vision-based 3D acquisition methods including both passive and active methods have a limitation in that cameras must be able to observe the surface to be measured. If this is not possible, that is to say, if the surface is occluded, most of the methods cannot acquire the surface shape. In this paper, we present a method that can acquire the 3D points(More)