Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains (2105.14391v2)

Abstract: Tracking the 6D pose of objects in video sequences is important for robot manipulation. This work presents se(3)-TrackNet, a data-driven optimization approach for long term, 6D pose tracking. It aims to identify the optimal relative pose given the current RGB-D observation and a synthetic image conditioned on the previous best estimate and the object's model. The key contribution in this context is a novel neural network architecture, which appropriately disentangles the feature encoding to help reduce domain shift, and an effective 3D orientation representation via Lie Algebra. Consequently, even when the network is trained solely with synthetic data can work effectively over real images. Comprehensive experiments over multiple benchmarks show se(3)-TrackNet achieves consistently robust estimates and outperforms alternatives, even though they have been trained with real images. The approach runs in real time at 90.9Hz. Code, data and supplementary video for this project are available at https://github.com/wenbowen123/iros20-6d-pose-tracking

• The paper presents a novel calibration method that aligns synthetic and real image residuals to improve 6D pose tracking.
• It leverages a data-driven strategy to bridge domain gaps, achieving enhanced tracking accuracy on benchmark datasets.
• The research offers practical insights for deploying robust pose tracking systems in robotics and augmented reality applications.

Overview of CVPR Author Response Guidelines

This document delineates the guidelines and formatting requirements for authors submitting a rebuttal following the review of their papers at the Computer Vision and Pattern Recognition (CVPR) conference. The guidelines are part of a concerted effort to maintain consistency and clarity in the rebuttal process, which offers authors an opportunity to respond to reviewers’ comments without contributing additional experimental results or novel contributions.

Purpose and Scope

The rebuttal is designed to address factual errors and supply requested additional information rather than introducing new theorems, algorithms, or experimental results that were absent in the original submission. This guideline adheres to a policy established by the Pattern Analysis and Machine Intelligence Technical Committee (PAMI-TC) in 2018, which prohibits reviewers from requesting further experiments. Such constraints ensure that the rebuttal remains focused and within scope.

Formatting Specifications

The rebuttal must comply with stringent formatting criteria:

• Length and Layout: The document is limited to one page, using a two-column format. Overlength responses are not reviewed.
• Text and Font: Text must be set in Times or Times Roman, single-spaced, with particular emphasis on maintaining section heading consistency.
• Figures and References: Figures may be included to illustrate responses to reviewers’ comments, provided they do not present new results. Bibliographic references must be in 9-point Times and follow a specific citation format.

Practical Implications

The adherence to these guidelines is crucial in ensuring the rebuttal process remains efficient and fair. By restricting the inclusion of new experimental data, the focus remains on clarification and correction rather than extending the scope of the original research. This process reinforces the integrity of the review system and ensures that all authors adhere to a standardized process.

Theoretical Implications and Future Considerations

The constraints placed on the rebuttal process reflect broader discussions on reproducibility and transparency in computer science research. As the field continues to evolve, the principles underlying these guidelines may influence the development of similar policies in other academic conferences. The emphasis on factual accuracy and the exclusion of new experimental data may serve as a template for maintaining the credibility of peer review across disciplines.

Conclusion

In summary, this document provides a comprehensive framework for authors preparing rebuttals for CVPR. By following these established guidelines, the process remains efficient, transparent, and fair, contributing to the overall quality and reliability of academic discourse within the computer vision community. As AI research progresses, these guidelines may serve as a foundation for further refinement in the peer review process.