Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses (1905.04132v2)

Abstract: We present Neural-Guided RANSAC (NG-RANSAC), an extension to the classic RANSAC algorithm from robust optimization. NG-RANSAC uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets. Previous works use heuristic side-information like hand-crafted descriptor distance to guide hypothesis search. In contrast, we learn hypothesis search in a principled fashion that lets us optimize an arbitrary task loss during training, leading to large improvements on classic computer vision tasks. We present two further extensions to NG-RANSAC. Firstly, using the inlier count itself as training signal allows us to train neural guidance in a self-supervised fashion. Secondly, we combine neural guidance with differentiable RANSAC to build neural networks which focus on certain parts of the input data and make the output predictions as good as possible. We evaluate NG-RANSAC on a wide array of computer vision tasks, namely estimation of epipolar geometry, horizon line estimation and camera re-localization. We achieve superior or competitive results compared to state-of-the-art robust estimators, including very recent, learned ones.

• The paper introduces NG-RANSAC, which uses a neural network to predict observation weights and efficiently guide hypothesis sampling.
• It presents a self-supervised training method that relies on inlier counts, eliminating the need for ground-truth model parameters.
• NG-RANSAC outperforms traditional RANSAC in tasks like epipolar geometry estimation and camera re-localization by reducing iteration counts in high outlier conditions.

Overview of Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses

The paper entitled "Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses" by Eric Brachmann and Carsten Rother explores an innovative approach to enhancing the classic RANSAC algorithm with neural guidance. This paper introduces NG-RANSAC, which utilizes a neural network to predict the probability distribution over observations, thereby guiding the selection of minimal sets for model hypothesis generation in domains where traditional RANSAC struggles due to high outlier ratios.

Traditional RANSAC and its Limitations

RANSAC is a robust method designed to estimate model parameters in datasets where some observations are outliers. It iterates over randomly chosen minimal sets to form model hypotheses, subsequently ranking these based on their consensus with the entire dataset. However, in domains with substantial outlier presence, traditional RANSAC's performance deteriorates, as it requires an exponentially increasing number of iterations to secure an outlier-free sample.

Neural-Guided RANSAC (NG-RANSAC)

In NG-RANSAC, the authors propose guiding the hypothesis sampling using a neural network, which provides a weight (interpreted as the probability) for each observation. This technique shifts the paradigm from uniformly random sampling to informed sampling based on learned predictions, thereby reducing the necessary number of iterations to achieve satisfactory results. The predicted weights for observations effectively inform RANSAC where to sample to likely capture inlier sets.

Key Extensions and Evaluation

The paper outlines two important extensions of their methodology. Firstly, the authors demonstrate the capability of training the neural guidance in a self-supervised manner, utilizing the inlier count as the sole training signal. This extension reduces the dependency on ground truth model parameters during training. Secondly, they integrate neural guidance into differentiable RANSAC (DSAC), which allows for joint optimization of observation and sampling probabilities. NG-RANSAC and its variants are evaluated on several canonical computer vision tasks like epipolar geometry estimation, horizon line estimation, and camera re-localization.

Empirical Results

NG-RANSAC consistently demonstrated superior or competitive performance against state-of-the-art robust estimation methods, including learned approaches. For instance, in the task of essential matrix estimation for image pairs, NG-RANSAC showcased enhanced accuracy in domains with high outlier ratios compared to methods solely based on RANSAC or on classification of correspondences into inliers and outliers. Moreover, it was robust across different application scenarios with or without side information such as descriptor distances.

Implications and Future Directions

This research contributes a significant step forward in robust estimation by leveraging neural networks to optimize the hypothesis generation process directly. The flexibility of NG-RANSAC allows for broad applicability across various domains of computer vision that require robust parameter estimation in noisy conditions. Future work may investigate the integration of more sophisticated neural architectures or further diversify the types of model parameters estimated, expanding applicability to more complex systems beyond the scope explored in this paper. Additionally, exploring the benefits of combining NG-RANSAC with advanced differentiable components could enhance the end-to-end learning capacity in vision pipelines.

By enriching RANSAC with data-driven guidance, NG-RANSAC represents a pivotal development in bringing machine learning efficacy into traditional optimization problems, paving the way for more intelligent and efficient robust estimators.