Direct-PoseNet: Absolute Pose Regression with Photometric Consistency (2104.04073v2)

Abstract: We present a relocalization pipeline, which combines an absolute pose regression (APR) network with a novel view synthesis based direct matching module, offering superior accuracy while maintaining low inference time. Our contribution is twofold: i) we design a direct matching module that supplies a photometric supervision signal to refine the pose regression network via differentiable rendering; ii) we modify the rotation representation from the classical quaternion to SO(3) in pose regression, removing the need for balancing rotation and translation loss terms. As a result, our network Direct-PoseNet achieves state-of-the-art performance among all other single-image APR methods on the 7-Scenes benchmark and the LLFF dataset.

• The paper introduces Direct-PoseNet, an absolute pose regression network that enhances accuracy using photometric consistency achieved via a direct matching module with differentiable rendering.
• Direct-PoseNet achieves state-of-the-art accuracy on 7-Scenes and LLFF datasets, showing median translation errors as low as 0.16m and rotation errors of 5.17 degrees on 7-Scenes.
• This method advances absolute pose regression for applications like augmented reality and robotics and demonstrates the potential of differentiable rendering for improving pose accuracy.

Absolute Pose Regression: Enhancing Accuracy with Photometric Consistency

The paper "Direct-PoseNet: Absolute Pose Regression with Photometric Consistency" presents an advanced methodology for performing absolute pose regression (APR) using single images, contributing significantly to the ongoing research in camera localization within computer vision and robotics. The relocalization pipeline crafted in this work integrates a direct matching module with a pose regression network, enhanced with photometric consistency, thus achieving state-of-the-art performance on both the 7-Scenes benchmark and LLFF dataset.

Contributions and Methodological Innovations

1. Photometric Supervision through Differentiable Rendering: The paper pioneers the use of photometric consistency in APR by developing a direct matching module that utilizes differentiable rendering for photometric supervision. This novel approach benefits from a rendering system based on neural radiance fields (NeRFs), providing a differentiable framework to enforce direct matching constraints. The APR network is thereby supervised not only by traditional pose regression loss but also by photometric loss, enhancing the accuracy of pose predictions.
2. Leverage Unlabeled Data: A standout feature highlighted in the paper is the approach's capability to utilize additional unlabeled data effectively. Unlike previous methods that rely on external supervision such as visual odometry or pose graph optimization, the direct matching module in Direct-PoseNet incorporates unlabeled images by minimizing photometric loss, refining pose regression performance without complex supervision mechanisms.

The method improves the APR framework by combing learning efficiency and superior accuracy. Through the photometric consistency, spatial cues are effectively integrated to guide the pose prediction process. These advancements underscore the paper's contribution towards bridging the gap between APR efficiency, simplicity, and accuracy.

Experimental Results and Performance Insights

The experimental evaluation underscores Direct-PoseNet's remarkable performance across different benchmarks. On the 7-Scenes dataset, the approach achieves superior accuracy with median errors reaching as low as 0.16 meters for translation and 5.17 degrees for rotation in the case of unlabeled data training—a notable improvement over previous benchmarks. Furthermore, the LLFF dataset trials demonstrate this method's reliability in complex real-world scenarios, achieving top-tier performance when exceeding common error thresholds.

The use of coarse-to-fine positional encoding within the NeRF framework, discussed in the ablation studies, mitigates reconstruction artifacts, further enhancing the reliability of synthetic images used for training. This innovation guarantees robust performance across diverse scene types, even those exhibiting outward perspectives and varying photo conditions.

Implications and Future Directions

This paper positions itself as an important stepping stone in the field of APR techniques, showing promise for practical applications in areas such as augmented reality, robotics, and autonomous navigation. Apart from its immediate practical applications, its methodological enhancements invite further research into the incorporation of differentiable rendering for broader machine learning tasks.

Future studies could take inspiration from Direct-PoseNet to explore these avenues, particularly focusing on expanding scalability to larger outdoor scenes, dynamic environments, or enhancing integration with quality improvements in neural rendering technologies. Continued research could also leverage the pipeline's principles to explore real-time applications using consumer-grade devices with limited hardware capabilities.

In summary, "Direct-PoseNet: Absolute Pose Regression with Photometric Consistency" delineates a powerful approach to APR, implementing photometric supervision to refine pose accuracy without requiring complex external systems. This paper significantly advances the capabilities of APR models, creating a foundation for future innovations in AI-driven camera localization.