Lazy Visual Localization via Motion Averaging
Siyan Dong Shaohui Liu Hengkai Guo Baoquan Chen Marc Pollefeys
Visual (re)localization is critical for various applications in computer
vision and robotics. Its goal is to estimate the 6 degrees of freedom (DoF)
camera pose for each query image, based on a set of posed database images.
Currently, all leading solutions are structure-based that either explicitly
construct 3D metric maps from the database with structure-from-motion, or
implicitly encode the 3D information with scene coordinate regression models.
On the contrary, visual localization without reconstructing the scene in 3D
offers clear benefits. It makes deployment more convenient by reducing database
pre-processing time, releasing storage requirements, and remaining unaffected
by imperfect reconstruction, etc. In this technical report, we demonstrate that
it is possible to achieve high localization accuracy without reconstructing the
scene from the database. The key to achieving this owes to a tailored motion
averaging over database-query pairs. Experiments show that our visual
localization proposal, LazyLoc, achieves comparable performance against
state-of-the-art structure-based methods. Furthermore, we showcase the
versatility of LazyLoc, which can be easily extended to handle complex
configurations such as multi-query co-localization and camera rigs.