Three-dimensional Shape Recovery Research Articles

Algorithms for simple object reconstruction using the largest possible object approach

SUMMARYRecently, three-dimensional motion analysis and shape recovery have attracted growing attention as promising avenues of approach to image understanding, object reconstruction as well as computer vision for robotic Systems. The image generation problem and the model generation problem are presented. More specifically, the inputs to the image generator are an old image, object model, motion specification, and hidden line and hidden surface algorithms. The output is a new image. Since the object model is given, the top-down approach is usually used. On the other hand, for the model generation problem, the input is an image sequence while the output is an object model. Since the object model is not given, and bottom-up approach is usually used.In this paper, the largest possible object approach is proposed and the advantages of this approach are stated. They are:1. This approach may be applicable to objects with planar surfaces as well as nonplanar surfaces.2. This approach may be applicable to the case that there are more than one face change per frame.3. This approach may be applicable when the camera is moving.4. This approach may be applicable when the object is measured by several measuring stations.By using this approach, algorithms for simple object reconstruction given a sequence of pictures are presented together with illustrative examples. The relevance and importance of this work are discussed.The results of this paper may have useful applications in object reconstruction, pictorial data reduction and computer vision for robotic Systems.

Invariant properties of straight homogeneous generalized cylinders and their contours

A fundamental group in computer vision is the recovery of three-dimensional shape from image data. While this problem is in general underconstrained, the authors show that it can be simplified in the case where the objects being viewed are generalized cylinders. They consider the class of straight homogeneous generalized cylinders (SHGCs), without any further assumption on the viewing direction or the precise shape of these objects. They present a rigorous mathematical study of the geometry of SHGCs and characterize their Gaussian curvature and occluding contours, and use these results to prove several new invariant properties of the contours of SHGCs. These properties are, in turn, used in two implemented algorithms for recovering SHGC descriptions from image contours. Several examples of segmentation of real images are given. Other applications are also discussed. >

A visual navigation system for autonomous land vehicles

A modular system architecture has been developed to support visual navigation by an autonomous land vehicle. The system consists of vision modules performing image processing, three-dimensional shape recovery, and geometric reasoning, as well as modules for planning, navigating, and piloting. The system runs in two distinct modes, bootstrap and feedforward. The bootstrap mode requires analysis of entire images to find and model the objects of interest in the scene (e.g., roads). In the feedforward mode (while the vehicle is moving), attention is focused on small parts of the visual field as determined by prior views of the scene, to continue to track and model the objects of interest. General navigational tasks are decomposed into three categories, all of which contribute to planning a vehicle path. They are called long-, intermediate-, and short-range navigation, reflecting the scale to which they apply. The system has been implemented as a set of concurrent communicating modules and used to drive a camera (carried by a robot arm) over a scale model road network on a terrain board. A large subset of the system has been reimplemented on a VICOM image processor and has driven the DARPA Autonomous Land Vehicle (ALV) at Martin Marietta's test site in Denver, CO.

Robot vision

Robot vision is widely studied in Japan to realize flexible manufacturing systems. The study of robot vision includes the development of the following themes: input devices for range information, feature extraction for inspection or positioning, high-speed image processors, stereo vision, three-dimensional shape recovery, object recognition by model matching, and so forth. This paper describes some interesting work in these fields.