  We built our pipeline EgoLoc-v1, mainly inspired by EgoLoc. We propose a
model ensemble strategy to improve the camera pose estimation part of the VQ3D
task, which has been proven to be essential in previous work. The core idea is
not only to do SfM for egocentric videos but also to do 2D-3D matching between
existing 3D scans and 2D video frames. In this way, we have a hybrid SfM and
camera relocalization pipeline, which can provide us with more camera poses,
leading to higher QwP and overall success rate. Our method achieves the best
performance regarding the most important metric, the overall success rate. We
surpass previous state-of-the-art, the competitive EgoLoc, by $1.5\%$. The code
is available at \url{https://github.com/Wayne-Mai/egoloc_v1}.
