Relative Camera Pose Estimation Using Convolutional Neural Networks (1702.01381v3)

Abstract: This paper presents a convolutional neural network based approach for estimating the relative pose between two cameras. The proposed network takes RGB images from both cameras as input and directly produces the relative rotation and translation as output. The system is trained in an end-to-end manner utilising transfer learning from a large scale classification dataset. The introduced approach is compared with widely used local feature based methods (SURF, ORB) and the results indicate a clear improvement over the baseline. In addition, a variant of the proposed architecture containing a spatial pyramid pooling (SPP) layer is evaluated and shown to further improve the performance.

• The paper introduces a novel end-to-end CNN architecture using Siamese network configurations for estimating relative camera pose directly from image pairs.
• The CNN method outperforms traditional feature-based approaches, like SURF and ORB, particularly in challenging scenes with textureless areas or repetitive patterns.
• Experimental results on the DTU dataset demonstrate that incorporating a spatial pyramid pooling layer enables higher resolution processing and significantly improves estimation accuracy.

Overview of "Relative Camera Pose Estimation Using Convolutional Neural Networks"

The paper under discussion introduces a method for estimating the relative pose between two cameras using convolutional neural networks (CNNs). This research addresses a fundamental computer vision problem critical to numerous applications, such as Structure from Motion (SfM), Simultaneous Localization and Mapping (SLAM), and visual odometry. Traditional methods, relying heavily on local feature detection and matching, often struggle in environments characterized by repetitive patterns, textureless objects, or significant viewpoint changes. The proposed CNN-based system presents an alternative approach, processing RGB images to directly estimate relative rotation and translation.

Key Contributions

The authors outline several important contributions:

1. Proposed CNN Architecture: The paper describes a novel end-to-end CNN architecture capable of estimating relative camera poses, consisting of Siamese network architecture configurations. Each branch processes an image from a camera pair, ultimately regressing to a seven-dimensional pose vector made up of a quaternion for orientation and a 3D vector for translation.
2. Comparison with Traditional Methods: The CNN-based method is benchmarked against standard feature-based techniques such as SURF and ORB. These comparisons are crucial in showcasing the superiority of the CNN approach in complex scenarios where traditional methods are prone to failure due to their reliance on hand-crafted features.
3. Experimental Evaluation and High-Resolution Processing: An experimental evaluation is conducted on the DTU Robot Image Dataset, renowned for its precision due to the controlled nature of its data collection process. The results demonstrate that incorporating a spatial pyramid pooling (SPP) layer allows CNN models to process images at higher resolutions, which significantly enhances estimation accuracy.

Performance and Results

The team observes an improvement in pose estimation accuracy when compared with established feature-based methods. Their method outperforms conventional methods in determining relative orientations and performs competitively in relative translations, particularly in cases where input images are textureless or contain reflective surfaces. Through transfer learning, they leverage pre-trained models on a large-scale classification dataset, underscoring the adaptability and robustness of CNNs in this domain.

Implications

This work has noteworthy implications:

• Practical Applications: Enhanced camera pose estimation is instrumental for improved performance in SfM, SLAM, and other computer vision tasks requiring spatial understanding. The shift from reliance on local feature matching towards utilizing holistic image information marks a substantial methodological advancement.
• Future Research Directions: The paper opens new avenues for future research. The potential for further refinement and scaling of such networks could lead to even greater improvements in estimation accuracy. Moreover, the combination with other machine learning paradigms, such as unsupervised or semi-supervised methods, could enhance training efficiency and accuracy.

Concluding Thoughts

In conclusion, this paper presents a compelling CNN-based alternative to traditional feature-based relative camera pose estimation. It highlights both the capability and limitations of CNN architectures in extracting spatial relationships from image pairs. While offering a significant performance boost in problematic scenarios for traditional methods, it also sets the stage for future enhancements leveraging advancements in deep learning frameworks and architectures.