用可微分鲁棒损失实现更稳定的两视图几何估计
Robust Two-View Geometry Estimation with Implicit Differentiation
- 将鲁棒基础矩阵估计建模为隐式层，避免反向传播延迟
- 引入置信度相关可学习权重，提升特征匹配信息利用率
- 端到端联合训练特征提取、匹配与几何估计，适合高精度定位任务
我们提出一种基于可微分鲁棒损失函数拟合的新颖两视图几何估计框架。将鲁棒基础矩阵估计视为隐式层，避免了时间反向传播，显著提升了数值稳定性。为充分利用特征匹配阶段的信息，引入依赖匹配置信度的可学习权重。该方法将特征提取、匹配与两视图几何估计统一于一个端到端可训练的流程中。在室外与室内场景下的相机位姿估计任务上进行评估，多个数据集上的实验表明，该方法相比经典及学习型最先进方法均有显著优势。
原文摘要 · Abstract (English)
We present a novel two-view geometry estimation framework which is based on a differentiable robust loss function fitting. We propose to treat the robust fundamental matrix estimation as an implicit layer, which allows us to avoid backpropagation through time and significantly improves the numerical stability. To take full advantage of the information from the feature matching stage we incorporate learnable weights that depend on the matching confidences. In this way our solution brings together feature extraction, matching and two-view geometry estimation in a unified end-to-end trainable pipeline. We evaluate our approach on the camera pose estimation task in both outdoor and indoor scenarios. The experiments on several datasets show that the proposed method outperforms both classic and learning-based state-of-the-art methods by a large margin. The project webpage is available at: https://github.com/VladPyatov/ihls
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