A model-based three-dimensional (3-D) vision system is introduced. Range data of objects in a scene are recovered by passive trinocular stereo. Two depth maps are independently produced from two pairs of stereo images, and 3-D points are obtained by merging the two depth maps. The scene description method is as follows. At first, short segments are formed from the 3-D points which satisfy a proximity condition. The condition is that both the 3-D distance between two points and the 2-D distance between corresponding 2-D points on an edge image should be less than each threshold. Next, scene features are extracted by connecting the short segments successively with the proximity and direction conditions. The representations of 3-D objects are built by the solid model based on surface boundary representations. The model is extended from a conventional solid model on the geometrical representations. Shape matching is performed by a hypothesis and verification method. At first, two prominent scene features are matched to the stored model features. And then, the matching is verified with other scene features. The re-sults of matching are used to determine the location and the orientation of the object in 3-D space. Experimental results with a complex object are shown.