Absolute pose regressor (APR) networks are trained to estimate the pose of
the camera given a captured image. They compute latent image representations
from which the camera position and orientation are regressed. APRs provide a
different tradeoff between localization accuracy, runtime, and memory, compared
to structure-based localization schemes that provide state-of-the-art accuracy.
In this work, we introduce Camera Pose Auto-Encoders (PAEs), multilayer
perceptrons that are trained via a Teacher-Student approach to encode camera
poses using APRs as their teachers. We show that the resulting latent pose
representations can closely reproduce APR performance and demonstrate their
effectiveness for related tasks. Specifically, we propose a light-weight
test-time optimization in which the closest train poses are encoded and used to
refine camera position estimation. This procedure achieves a new
state-of-the-art position accuracy for APRs, on both the CambridgeLandmarks and
7Scenes benchmarks. We also show that train images can be reconstructed from
the learned pose encoding, paving the way for integrating visual information
from the train set at a low memory cost. Our code and pre-trained models are
available at this https URL.

本文介绍相机姿态自编码器（PAEs）的使用，在使用 APR 作为教师模型的情况下，使用教师 - 学生方法对其进行多层感知机神经网络的训练。我们表明，使用这种编码器时，可以获得与 APR 相当的姿态表示效果，并且展示了在相关任务中它们的有效性，同时提出了一种轻量级测试时间优化方法。通过对数据集 CambridgeLandmarks 和 7Scenes 的测试，该方法取得了新的最佳位置精度。