SupeRANSAC: One RANSAC to Rule Them All (2506.04803v1)

Abstract: Robust estimation is a cornerstone in computer vision, particularly for tasks like Structure-from-Motion and Simultaneous Localization and Mapping. RANSAC and its variants are the gold standard for estimating geometric models (e.g., homographies, relative/absolute poses) from outlier-contaminated data. Despite RANSAC's apparent simplicity, achieving consistently high performance across different problems is challenging. While recent research often focuses on improving specific RANSAC components (e.g., sampling, scoring), overall performance is frequently more influenced by the "bells and whistles" (i.e., the implementation details and problem-specific optimizations) within a given library. Popular frameworks like OpenCV and PoseLib demonstrate varying performance, excelling in some tasks but lagging in others. We introduce SupeRANSAC, a novel unified RANSAC pipeline, and provide a detailed analysis of the techniques that make RANSAC effective for specific vision tasks, including homography, fundamental/essential matrix, and absolute/rigid pose estimation. SupeRANSAC is designed for consistent accuracy across these tasks, improving upon the best existing methods by, for example, 6 AUC points on average for fundamental matrix estimation. We demonstrate significant performance improvements over the state-of-the-art on multiple problems and datasets. Code: https://github.com/danini/superansac

• The paper presents a unified RANSAC pipeline that enhances geometric model estimation across various vision tasks.
• It employs advanced sampling, robust degeneracy checks, and a MAGSAC++-based scoring function to effectively handle noise.
• Empirical evaluation on 11 large-scale datasets shows a 6-point AUC gain, setting a new benchmark in robust estimation.

SupeRANSAC: One RANSAC to Rule Them All

The paper "SupeRANSAC: One RANSAC to Rule Them All" presents a novel approach to robust estimation processes in computer vision, specifically addressing the challenges inherent in using the Random Sample Consensus (RANSAC) algorithm and its variants for model estimation in noisy, outlier-dominated environments. The research encapsulates a unified pipeline that consistently achieves high accuracy across a variety of geometric estimation problems, such as homographies, fundamental and essential matrices, and rigid and absolute pose estimation.

Core Contributions

The paper identifies major discrepancies in performance across existing frameworks, such as OpenCV and PoseLib, highlighting that beyond improving specific RANSAC components like sampling and scoring, the implementation details often play a crucial role in determining the efficacy of the pipeline. SupeRANSAC is designed to offer consistent enhancements over current state-of-the-art methodologies.

Methodological Insights

The SupeRANSAC framework integrates multiple layers of optimization and verification to ensure robust model accuracy. Key aspects include:

1. Sampling Techniques: SupeRANSAC employs PROSAC and Progressive NAPSAC, enhancing the probability of early model discovery by leveraging quality-driven and spatial locality considerations. This strategy is tailored to each problem, ensuring robustness whether the issue at hand is a homography or epipolar geometry estimation.
2. Degeneracy Checks: Efficient checks for sample and model degeneracy are embedded within the framework, particularly for two-view geometry problems where geometric viability is crucial. These checks ensure that computational resources are allocated effectively, avoiding ill-conditioned samples.
3. Model Scoring: Adopting a MAGSAC++-based scoring function, SupeRANSAC mitigates the influence of fixed thresholds by marginalizing noise scales. This provides robustness against varying noise distributions, a major improvement over traditional inlier counting methods.
4. Local and Final Optimization: The pipeline uses advanced optimization strategies, including GC-RANSAC for local optimization and iteratively reweighted least squares for final refinement. These approaches ensure the discovery of robust models, effectively handling large-scale datasets.
5. Nonminimal Solver Utilization: SupeRANSAC employs specific nonminimal solvers that improve on the efficiency and accuracy of model estimation from larger-than-minimal samples, ensuring that the framework consistently produces superior results across different tasks.

Empirical Evaluation

The framework was thoroughly evaluated on 11 large-scale public datasets, showcasing significant improvements over existing frameworks. SupeRANSAC yielded a 6-point AUC gain on average for fundamental matrix estimation, demonstrating its effectiveness in broad real-world applications, including tasks requiring homography or pose estimation in Structure-from-Motion and Simultaneous Localization and Mapping.

Implications and Future Directions

The introduction of SupeRANSAC offers substantial benefits both theoretically and practically. It presents a robust pipeline that can potentially reduce the complex and computationally expensive tuning of RANSAC variants in computer vision applications. By aligning problem-specific details and optimizations, SupeRANSAC sets a new benchmark in robust estimation accuracy and efficiency.

In the field of AI, the future development following SupeRANSAC involves adapting its principles to upcoming challenges, such as more generalized differentiable frameworks, enabling seamless integration with deep learning pipelines. Exploring unsupervised learning methods to enhance model discovery within RANSAC logic remains a promising avenue, potentially leading to even more autonomous systems capable of handling unseen data complexities.

In conclusion, the SupeRANSAC framework not only advances the robustness of geometric estimation in vision tasks but also emphasizes the significance of comprehensive pipeline design. As researchers continue to leverage these insights, the scope for AI's precision and reliability in task resolution will likely expand significantly, fostering deeper integrations across varied computational fields and applications.