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Visual Attention and Color Cues for 6D Pose Estimation on Occluded Scenarios Using RGB-D Data.

Joel Vidal^1,2, Chyi-Yeu Lin^2,3,4, Robert Martí¹

¹Computer Vision and Robotics Institute, University of Girona, 17003 Girona, Spain.

Sensors (Basel, Switzerland)

|December 10, 2021

Summary

This study enhances 6D pose estimation for occluded objects using visual attention and color cues. The new method significantly improves recognition rates, especially for partially visible objects in challenging scenarios.

Keywords:

3D object recognition 6D pose estimation RGB-D data computer vision model-based vision scene understanding

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Area of Science:

Computer Vision
Robotics
Machine Learning

Background:

6D pose estimation methods struggle with occluded objects, leading to performance degradation.
Existing techniques show limitations in recognizing objects with significant visual occlusion.

Purpose of the Study:

To improve the performance of state-of-the-art 6D pose estimation in occluded scenarios.
To leverage visual attention and color information to address occlusion challenges.

Main Methods:

Integration of top-down visual attention mechanisms.
Utilization of color cues for point detection, feature matching, and fitting score refinement.
Evaluation on multiple benchmark datasets including LM-O, TUD-L, IC-MI, and IC-BIN.

Main Results:

Significant performance boost for highly occluded objects (up to 30% improvement at 40-50% occlusion).
Robustness demonstrated across varying illumination conditions and multiple object instances.
Achieved high recall rates: 71% (LM-O), 92% (TUD-L), 99.3% (IC-MI), and 97.5% (IC-BIN).

Conclusions:

The proposed method effectively enhances 6D pose estimation accuracy under occlusion.
Visual attention and color cues are crucial for robust performance in cluttered and occluded environments.
The approach offers a substantial improvement over existing methods for challenging real-world scenarios.