Environmental recognition using images is a worthwhile research subject. 3D information is valuable information to recognize surrounding, because currently it is difficult for machines to understand 3D structure in the scene with only one image, even though a human can understand the scene structure from a picture. 3D information can be obtained from stereo and motion disparities. Since the stereo camera is assumed to be calibrated, the 3D shape can be calculated from disparities. On the other ahnd, since the camera motion, so called ego-motion, is not known, even though motion disparities is obtained,3D shape cannot be calculated until ego-motion is estimated. In addition, the 3D shape recovered from motion disparities is determied up to a scale. Generally, the accuracy of 3D reconstruction by stereo camera depends on the baseline. A stereo camera usually cannot take long baseline, while camera motion can produce long baseline. Since each stereo and motion has information on 3D shape, the combination of stereo and motion disparities could complementally produce better 3D reconstruction than only one of them. Researches in this report were taken placed toward the 3D reconstruction using stereo and motion information.