Absolute Pose Regression (APR) methods use deep neural networks to directly regress camera poses from RGB images. However, the predominant APR architectures only rely on 2D operations during inference, resulting in limited accuracy of pose estimation due to the lack of 3D geometry constraints or priors. In this work, we propose a test-time refinement pipeline that leverages implicit geometric constraints using a robust feature field to enhance the ability of APR methods to use 3D information during inference. We also introduce a novel Neural Feature Synthesizer (NeFeS) model, which encodes 3D geometric features during training and directly renders dense novel view features at test time to refine APR methods. To enhance the robustness of our model, we introduce a feature fusion module and a progressive training strategy. Our proposed method achieves state-of-the-art single-image APR accuracy on indoor and outdoor datasets.

翻译：绝对位姿回归（APR）方法利用深度神经网络直接从RGB图像中回归相机位姿。然而，主流的APR架构在推理过程中仅依赖二维操作，由于缺乏三维几何约束或先验信息，导致位姿估计精度有限。本文提出了一种测试时精炼流水线，通过利用鲁棒特征场隐式引入几何约束，增强APR方法在推理过程中使用三维信息的能力。同时，我们引入了一种新型神经特征合成器（NeFeS）模型，该模型在训练过程中编码三维几何特征，并在测试时直接渲染密集的新视角特征以精炼APR方法。为增强模型鲁棒性，我们提出了特征融合模块与渐进式训练策略。所提方法在室内外数据集上均实现了当前最优的单幅图像APR精度。