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Related Experiment Video

Updated: Jun 29, 2026

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Published on: December 4, 2013

3-D object recognition using 2-D views.

Wenjing Li¹, George Bebis, Nikolaos G Bourbakis

¹Computer Science and Engineering Department, University of Nevada, Reno, NV 89557, USA. wli@sti-hawaii.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|October 16, 2008

Summary

This summary is machine-generated.

This study enhances 3-D object recognition by integrating Algebraic Functions of Views (AFoVs) with indexing and learning, improving model-based recognition from 2-D images. The novel approach effectively predicts object appearance across viewpoints for accurate localization and verification.

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Published on: December 4, 2013

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

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Published on: October 18, 2024

Area of Science:

Computer Vision
Machine Learning
Robotics

Background:

Model-based 3-D object recognition from 2-D images is challenging due to viewpoint variations.
Existing category-based methods differ from the goal of recognizing specific object instances.

Purpose of the Study:

To improve 3-D object recognition and localization using geometric models.
To integrate Algebraic Functions of Views (AFoVs) with indexing and learning for enhanced performance.

Main Methods:

Computed the space of views using AFoVs and object features, combined with indexing and learning in two stages.
Employed rigidity constraints to eliminate unrealistic views and the Expectation-Maximization (EM) algorithm with Random Projection (RP) for learning.
Developed a hybrid framework using geometric knowledge for hypothesizing and geometric/intensity information for verification.

Main Results:

Demonstrated promising performance on both artificial and real data for 3-D object recognition.
Showcased extensions of the AFoVs framework to predict object intensity appearance.

Conclusions:

The integrated AFoVs, indexing, and learning approach effectively addresses model-based 3-D object recognition.
The hybrid framework shows potential for robust object recognition by combining geometric and appearance information.