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Related Experiment Video

Updated: Jun 20, 2026

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data

Published on: October 18, 2024

Shape and motion reconstruction from 3D-to-1D orthographically projected data via object-image relations.

Matthew Ferrara¹, Gregory Arnold, Mark Stuff

¹Air Force Research Laboratory, Dayton, OH 45433, USA. matthew.ferrera@afrl.af.mil

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 22, 2009

Summary

This study presents a novel algorithm for reconstructing 3D shapes and motion from range data, simplifying previous methods. It offers a more efficient and straightforward approach for echo-based range imaging applications.

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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

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Last Updated: Jun 20, 2026

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data

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Published on: October 18, 2024

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Area of Science:

Computer Vision
Robotics
3D Reconstruction

Background:

Reconstructing 3D shape and motion from 2D projections is a fundamental problem in computer vision.
Existing methods for range data often rely on uniqueness constraints or iterative initialization, limiting their applicability.

Purpose of the Study:

To develop an invariant-based algorithm for 3D shape and motion reconstruction from 1D orthographic range data.
To simplify and unify previous approaches by removing the need for uniqueness constraints and initialization.

Main Methods:

An invariant-based algorithm is proposed that exploits the object-image relation in echo-based range data.
The method processes all projections simultaneously without requiring an initialization step.
It is independent of translation removal processes like centroid removal or range alignment.

Main Results:

The algorithm offers a simplified and unified approach compared to prior work.
It requires fewer calculations and is more straightforward than existing methods.
Demonstrated as a natural extension of Tomasi and Kanade's 3D-to-2D method.

Conclusions:

The developed algorithm provides an efficient and robust solution for 3D shape and motion reconstruction from range data.
Its independence from uniqueness constraints and initialization makes it broadly applicable, including in inverse synthetic aperture radar imaging.