Robotics Research Technical Report: 3-D Curve Matching (Classic Reprint)

Eyal Kishon

ISBN 10: 1330240677 / ISBN 13: 9781330240670

Published by Forgotten Books, 2022

About this Item

Print on Demand. This book addresses the problem of efficiently finding the best subcurve match for two given 3D curves, an important task in robot vision and object recognition. The author presents an innovative algorithm that operates in time linear to the number of sample points on the observed curve, making it practical for real-time applications. The method is particularly valuable in situations where viewpoint and occlusion limit the data available and a full surface description is impractical. The author demonstrates the algorithm's effectiveness through experiments involving the matching of 3D curves extracted from real-world objects. The insights gained from this work have significant implications for the development of efficient and robust object recognition systems. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book.

Seller Inventory # 9781330240670_0

Bibliographic Details

• Title: Robotics Research Technical Report: 3-D Curve Matching (Classic Reprint)
• Publisher: Forgotten Books
• Publication Date: 2022
• Language: English
• ISBN 10: 1330240677
• ISBN 13: 9781330240670
• Binding: Paperback
• Condition: New
• Book Type: print-on-demand item
• Seller catalogs:
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About this title

Synopsis

Explore how to recognize and describe 3-D objects through curves

This book presents a practical approach to matching 3-D curves to identify and position object parts, even when views overlap or objects are partially hidden. It combines theory with step-by-step methods that work in real time.

The work focuses on turning complex 3-D shapes into sequences of local, invariant curve signatures. It explains how to use these footprints to filter candidates, then apply robust 3-D subcurve matching to confirm the best fit. Realistic applications include reassembling broken objects and building full object descriptions from partial views, using hands-on experiments with 3-D curve data.
What you’ll experience

• How to represent curves with local, rotationally and translationally invariant signatures.
• The idea behind geometric hashing to identify long matching subcurves.
• How the Schwartz–Sharir algorithm is used to compute alignments in 3-D space.
• Practical workflows for 3-D curve reconstruction from multiple viewpoints.

Ideal for readers who want a hands-on introduction to 3-D curve matching, object recognition, and curve-based model construction. It’s especially relevant for researchers and practitioners in robotics, computer vision, and CAD.