Convolutional neural network architecture for geometric matching (1703.05593v2)

Abstract: We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters. The contributions of this work are three-fold. First, we propose a convolutional neural network architecture for geometric matching. The architecture is based on three main components that mimic the standard steps of feature extraction, matching and simultaneous inlier detection and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never seen before images. Finally, we show that the same model can perform both instance-level and category-level matching giving state-of-the-art results on the challenging Proposal Flow dataset.

• The paper introduces an end-to-end CNN architecture that mimics classic correspondence workflows using differentiable feature extraction, matching, and regression stages.
• It leverages synthetic training data to eliminate manual annotations, achieving state-of-the-art performance on the Proposal Flow dataset.
• The framework efficiently handles both affine and non-rigid transformations, managing intra-class variations and background clutter for improved matching accuracy.

Convolutional Neural Network Architecture for Geometric Matching

The paper "Convolutional Neural Network Architecture for Geometric Matching" introduces a novel convolutional neural network (CNN) framework designed to address the challenge of estimating correspondences between image pairs under geometric transformations. The paper is rooted in traditional computer vision methodologies but advances the state-of-the-art by leveraging deep learning to improve accuracy and generalization, particularly in cases involving significant appearance changes and complex deformations.

Core Contributions

The authors delineate three key contributions:

1. CNN-Based Geometric Matching Architecture: The proposed architecture comprises three stages: feature extraction, feature matching, and model parameter estimation. By mimicking classical correspondence workflows through differentiable modules, the design enables end-to-end training for geometric transformations such as affine and thin-plate spline (TPS).
2. Training on Synthetic Data: The network is trained using synthetic data without manual annotations, which is pivotal for scalability given the lack of large datasets annotated with precise geometric transformations. The ability to generalize from synthetic imagery is particularly emphasized.
3. Instance-Level and Category-Level Matching: The architecture demonstrates proficiency in both instance and category-level geometric matching, achieving state-of-the-art performance on the Proposal Flow dataset. Notably, the network effectively handles intra-class variations and background clutter.

Technical Highlights

• Feature Extraction: The use of pre-trained VGG-16 CNN features enhances the network's capacity to withstand drastic appearance changes. Feature maps are efficiently derived from image pairs via a siamese network configuration.
• Matching Network: A custom matching layer computes pairwise similarities between features across image pairs, followed by normalization. This approach accounts for ambiguous or incorrect matches, paralleling robustness checks in traditional methods.
• Regression Network: The authors employ a hybrid regression framework inspired by Hough voting and neighborhood consensus, resulting in accurate estimation of the geometric transformation parameters. The use of convolutional layers ensures scalability and computational efficiency.
• Transformation Hierarchy: To tackle complex transformations, the network estimates simpler affine maps before refining the alignment using TPS. This staged approach facilitates robust approximations of non-rigid transformations.

Results and Comparisons

The paper presents a comprehensive evaluation on the Proposal Flow dataset, showcasing superior PCK (probability of correct keypoint) scores compared to traditional methods such as RANSAC and other deep learning approaches. The ability to train with synthetic transformations substantiates the network's scalability and robustness. The architecture's competitive advantage lies in its generalization capacity that is largely invariant to training datasets.

Implications and Future Directions

The proposed methodology has significant implications for various computer vision applications, including image manipulation, 3D reconstruction, and semantic segmentation. By allowing the matching process to dynamically adapt through end-to-end learning, this work paves the way for further exploration of neural architectures designed for complex image matching tasks. Potential future developments could investigate extensions to handle more diverse geometric models, increasing the robustness across additional challenges like varying illumination conditions or cross-domain matching tasks.

In conclusion, this paper enhances the landscape of geometric matching by integrating traditional geometric insights with the power of deep learning, thereby setting a promising direction for future research and application in computer vision.