Robust Image Stitching With Optimal Plane.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Image Processing

Background:

• Image stitching aims to combine multiple images into a seamless panorama.
• Existing methods often struggle with robustness and naturalness, especially in complex scenes.
• Balancing content alignment and structural preservation remains a challenge.

Purpose of the Study:

• To develop an unsupervised deep image stitching framework named RopStitch.
• To improve both the robustness and naturalness of stitched images.
• To achieve generalizable performance across diverse, unseen real-world scenes.

Main Methods:

• Proposed a dual-branch architecture incorporating universal priors of content perception.
• Utilized a pretrained branch for semantic invariance and a learnable branch for fine-grained features.
• Introduced virtual optimal planes by estimating homography decomposition coefficients to resolve alignment-structure conflicts.
• Implemented an iterative coefficient predictor and minimal semantic distortion constraint.

Main Results:

• RopStitch demonstrated superior performance compared to existing methods.
• The framework achieved significant improvements in scene robustness.
• Enhanced naturalness of the stitched images was observed.
• The method showed highly generalizable performance across various datasets.

Conclusions:

• RopStitch offers a robust and natural unsupervised deep image stitching solution.
• The proposed dual-branch architecture and virtual optimal planes effectively address key challenges.
• The framework sets a new benchmark for image stitching in terms of robustness and naturalness.