SC-wLS: Towards Interpretable Feed-forward Camera Re-localization

• URL: http://arxiv.org/abs/2210.12748v1
• Date: Sun, 23 Oct 2022 15:15:48 GMT
• Title: SC-wLS: Towards Interpretable Feed-forward Camera Re-localization
• Authors: Xin Wu, Hao Zhao, Shunkai Li, Yingdian Cao, Hongbin Zha
• Abstract summary: Visual re-localization aims to recover camera poses in a known environment, which is vital for applications like robotics or augmented reality. Feed-forward absolute camera pose regression methods directly output poses by a network, but suffer from low accuracy. We propose a feed-forward method termed SC-wLS that exploits all scene coordinate estimates for weighted least squares pose regression.
• Score: 29.332038781334443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual re-localization aims to recover camera poses in a known environment, which is vital for applications like robotics or augmented reality. Feed-forward absolute camera pose regression methods directly output poses by a network, but suffer from low accuracy. Meanwhile, scene coordinate based methods are accurate, but need iterative RANSAC post-processing, which brings challenges to efficient end-to-end training and inference. In order to have the best of both worlds, we propose a feed-forward method termed SC-wLS that exploits all scene coordinate estimates for weighted least squares pose regression. This differentiable formulation exploits a weight network imposed on 2D-3D correspondences, and requires pose supervision only. Qualitative results demonstrate the interpretability of learned weights. Evaluations on 7Scenes and Cambridge datasets show significantly promoted performance when compared with former feed-forward counterparts. Moreover, our SC-wLS method enables a new capability: self-supervised test-time adaptation on the weight network. Codes and models are publicly available.

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