EgoCOL: Egocentric Camera pose estimation for Open-world 3D object
  Localization @Ego4D challenge 2023
Cristhian Forigua Maria Escobar Jordi Pont-Tuset Kevis-Kokitsi Maninis Pablo Arbeláez
We present EgoCOL, an egocentric camera pose estimation method for open-world
3D object localization. Our method leverages sparse camera pose reconstructions
in a two-fold manner, video and scan independently, to estimate the camera pose
of egocentric frames in 3D renders with high recall and precision. We
extensively evaluate our method on the Visual Query (VQ) 3D object localization
Ego4D benchmark. EgoCOL can estimate 62% and 59% more camera poses than the
Ego4D baseline in the Ego4D Visual Queries 3D Localization challenge at CVPR
2023 in the val and test sets, respectively. Our code is publicly available at
https://github.com/BCV-Uniandes/EgoCOL