GDRNPP: A Geometry-Guided and Fully Learning-Based Object Pose Estimator.

Area of Science:

• Computer Vision
• Robotics
• Deep Learning

Background:

• 6D pose estimation of rigid objects is a challenging computer vision problem.
• Current deep learning methods often combine Convolutional Neural Networks (CNNs) with traditional algorithms, leading to slower, non-end-to-end trainable systems.
• Existing direct pose regression networks show suboptimal performance, necessitating hybrid approaches.

Purpose of the Study:

• To develop a fully learning-based object pose estimator that is accurate and efficient.
• To investigate and improve upon both direct and indirect pose estimation methods.
• To create an end-to-end trainable system for 6D pose estimation from monocular images.

Main Methods:

• Introduced the Geometry-guided Direct Regression Network (GDRN) for end-to-end 6D pose learning from monocular images.
• Developed a geometry-guided pose refinement module using predicted coordinate maps and RGB-D data for enhanced accuracy.
• Built an end-to-end differentiable architecture for robust 3D-3D correspondences between observed and rendered images.

Main Results:

• The proposed GDRNPP pipeline achieved state-of-the-art performance on the BOP Challenge for two consecutive years.
• GDRNPP became the first method to outperform traditional techniques in both accuracy and speed for 6D pose estimation.
• The system demonstrated robust and accurate 3D-3D correspondences for pose refinement.

Conclusions:

• A fully learning-based approach can effectively address the challenges of 6D object pose estimation.
• The Geometry-guided Direct Regression Network (GDRN) and its enhancement GDRNPP offer a superior alternative to traditional hybrid methods.
• This work paves the way for faster and more accurate real-time pose estimation in computer vision applications.