论文标题
基于透视变形的多视图残差协方差的模型
A Model for Multi-View Residual Covariances based on Perspective Deformation
论文作者
论文摘要
在这项工作中，我们得出了一个模型，用于在多视图SFM，Odometry和SLAM设置中视觉残差的协方差。我们方法的核心是将残留协方差作为几何和光度噪声源的组合制定。而我们的主要新颖贡献是派生的术语模型，该术语模拟局部2D斑块在一个点附近的3D表面成像时如何遭受透视变形的影响。总之，这些加起来是一种有效且一般的公式，不仅可以提高基于特征和直接方法的准确性，而且还可以用来估计状态熵的更准确度量，从而更好地基础的点可见性阈值。我们使用合成和真实数据验证了模型，并将其集成到基于光度和功能的束调节中，从而通过可忽略不计的开销提高了其准确性。
In this work, we derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups. The core of our approach is the formulation of the residual covariances as a combination of geometric and photometric noise sources. And our key novel contribution is the derivation of a term modelling how local 2D patches suffer from perspective deformation when imaging 3D surfaces around a point. Together, these add up to an efficient and general formulation which not only improves the accuracy of both feature-based and direct methods, but can also be used to estimate more accurate measures of the state entropy and hence better founded point visibility thresholds. We validate our model with synthetic and real data and integrate it into photometric and feature-based Bundle Adjustment, improving their accuracy with a negligible overhead.
