Image Matching Filtering and Refinement by Planes and Beyond

2024-11-14

arXiv.org

AI Summary

This work addresses outlier-robust filtering and keypoint refinement for sparse image matching without deep learning, under the assumption that motion flow is locally modeled by homographies. We propose a modular geometric framework grounded in virtual planar surfaces: (i) iterative RANSAC clusters planar motion hypotheses and removes outliers; (ii) keypoint locations are refined via reprojection constraints and template matching; and (iii) an intermediate homographic distortion minimization mechanism enhances adaptability to non-planar scenes. To our knowledge, this is the first framework unifying planar clustering, cross-patch correlation-based refinement, and intermediate homography optimization. It remains highly robust even with unknown camera intrinsics. Evaluated on standard benchmarks and uncalibrated real-world imagery, it matches or surpasses state-of-the-art deep learning methods—demonstrating the efficacy and scalability of geometry-driven matching within practical image processing pipelines.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

This paper introduces a modular, non-deep learning method for filtering and refining sparse correspondences in image matching. Assuming that motion flow within the scene can be approximated by local homography transformations, matches are aggregated into overlapping clusters corresponding to virtual planes using an iterative RANSAC-based approach, with non-conforming correspondences discarded. Moreover, the underlying planar structural design provides an explicit map between local patches associated with the matches, enabling optional refinement of keypoint positions through cross-correlation template matching after patch reprojection. Finally, to enhance robustness and fault-tolerance against violations of the piece-wise planar approximation assumption, a further strategy is designed for minimizing relative patch distortion in the plane reprojection by introducing an intermediate homography that projects both patches into a common plane. The proposed method is extensively evaluated on standard datasets and image matching pipelines, and compared with state-of-the-art approaches. Unlike other current comparisons, the proposed benchmark also takes into account the more general, real, and practical cases where camera intrinsics are unavailable. Experimental results demonstrate that our proposed non-deep learning, geometry-based approach achieves performances that are either superior to or on par with recent state-of-the-art deep learning methods. Finally, this study suggests that there are still development potential in actual image matching solutions in the considered research direction, which could be in the future incorporated in novel deep image matching architectures.

Problem

• Filters and refines sparse image correspondences using local homography transformations.
• Enhances robustness by minimizing patch distortion in plane reprojection.
• Evaluates performance against deep learning methods without camera intrinsics.

Innovation

• Uses RANSAC-based clustering for image matching.
• Refines keypoints via cross-correlation template matching.
• Minimizes distortion with intermediate homography projection.