Expert Sample Consensus Applied to Camera Re-Localization (1908.02484v1)

Abstract: Fitting model parameters to a set of noisy data points is a common problem in computer vision. In this work, we fit the 6D camera pose to a set of noisy correspondences between the 2D input image and a known 3D environment. We estimate these correspondences from the image using a neural network. Since the correspondences often contain outliers, we utilize a robust estimator such as Random Sample Consensus (RANSAC) or Differentiable RANSAC (DSAC) to fit the pose parameters. When the problem domain, e.g. the space of all 2D-3D correspondences, is large or ambiguous, a single network does not cover the domain well. Mixture of Experts (MoE) is a popular strategy to divide a problem domain among an ensemble of specialized networks, so called experts, where a gating network decides which expert is responsible for a given input. In this work, we introduce Expert Sample Consensus (ESAC), which integrates DSAC in a MoE. Our main technical contribution is an efficient method to train ESAC jointly and end-to-end. We demonstrate experimentally that ESAC handles two real-world problems better than competing methods, i.e. scalability and ambiguity. We apply ESAC to fitting simple geometric models to synthetic images, and to camera re-localization for difficult, real datasets.

• The paper introduces ESAC, a framework combining DSAC and MoE to improve camera pose estimation by efficiently sampling and selecting expert hypotheses.
• The method leverages an end-to-end training process that optimizes resource allocation among experts to handle ambiguous and noisy correspondence data effectively.
• Experimental results demonstrate that ESAC outperforms traditional RANSAC, exhibiting state-of-the-art accuracy on complex real-world indoor datasets.

Overview of "Expert Sample Consensus Applied to Camera Re-Localization"

In the paper "Expert Sample Consensus Applied to Camera Re-Localization," the authors introduce a novel method called Expert Sample Consensus (ESAC) to address camera re-localization challenges within the field of computer vision. This work builds upon the concept of fitting model parameters to noisy data, which is pivotal in computer vision applications. The paper specifically targets the estimation of the 6D camera pose using correspondences between a 2D image and a known 3D environment.

Technical Contributions

The authors present ESAC as a robust estimator that integrates Differentiable Sample Consensus (DSAC) with Mixture of Experts (MoE). This integration aims to improve the accuracy and efficiency of pose estimation by handling large and ambiguous problem domains more effectively. The introduction of ESAC provides two main technical contributions:

1. Efficient Training Method: The authors propose an end-to-end trainable system that efficiently combines DSAC with MoE. This method is designed to allocate computational resources judiciously among specialized experts, improving model hypothesis sampling and selection.
2. Application to Real-World Problems: They demonstrate ESAC's strength in managing scalability and ambiguity issues, specifically in the context of camera re-localization. The method shows state-of-the-art performance on complex datasets that involve indoor environments.

Numerical Results and Evaluation

The paper presents experimental evidence showing that ESAC surpasses existing methods in handling scalability and ambiguity. Test results highlight the method’s ability to improve the re-localization accuracy in environments with repeated structures and ambiguous geometry. Moreover, ESAC exhibits superior robustness compared to traditional RANSAC approaches, notably due to its effective distribution of hypotheses among a network of experts. These results are quantitatively backed by evaluations on synthetic data, as well as challenging real-world camera re-localization datasets.

Implications and Future Prospects

The proposed ESAC framework opens new avenues for dealing with complex visual learning tasks by marrying the probabilistic elegance of DSAC with the divide-and-conquer efficiency of MoE. Practically, this means more nuanced handling of ambiguous datasets, a critical challenge in scaling AI systems to real-world applications with heterogeneous data ecosystems. Theoretically, the introduction of ESAC could inspire further exploration into ensemble-based learning strategies within parametric model fitting and vision tasks beyond camera pose modeling.

Looking forward, future work might investigate extending ESAC into other domains of computer vision where ambiguity and scale are significant barriers. Additionally, refinement of gating networks and optimization strategies could further enhance the robustness and accuracy of ESAC in scenarios with higher complexity and data variance. Overall, the promising results position ESAC as a valuable contribution to both the practical toolkit and theoretical discourse around ensemble methods in visual computing.