Robust Estimation in Computer Vision
Robust estimation is key in computer vision to get reliable results even when faced with noisy or imperfect data.
Why it's Important
- Real-world visual data can have noise, be incomplete, or contain errors, often termed as "outliers."
- Standard algorithms might give incorrect results if outliers heavily influence them.
- Robust estimation methods reduce the impact of these outliers.
Model Fitting: In tasks such as feature matching or camera calibration, fitting a model to data points is common. Robust techniques, like RANSAC, help in finding the best fit while minimizing the influence of outliers.
Weighted Errors: Rather than treating all data points equally, robust estimation often assigns weights to data points. Points believed to be outliers might be given less weight, ensuring they don't overly influence the final result.
Applications: Robust estimation is widely used in many computer vision tasks, such as:
- Image stitching
- 3D reconstruction
- Motion analysis
- Object detection
Robust estimation ensures computer vision applications work reliably in real-world scenarios, even when faced with imperfect data. By giving less importance to outliers, it delivers more accurate and dependable results.
- Adaptive Reordering Sampler with Neurally Guided MAGSAC (ICCV 2023) [paper]
- Generalized Differentiable RANSAC (ICCV 2023) [paper]
- RLSAC: Reinforcement Learning Enhanced Sample Consensus for End-to-End Robust Estimation (ICCV 2023) [paper]
- Space-Partitioning RANSAC (ECCV 2022) [paper]
- Learning To Find Good Models in RANSAC [paper]
- VSAC: Efficient and Accurate Estimator for H and F [paper]