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EgoCOL: Egocentric Camera pose estimation for Open-world 3D object Localization @Ego4D challenge 2023
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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
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Cited by 1 Pith paper
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EgoHieraLoc: A Cortically Inspired Hierarchical Segmentation-Guided Framework for Egocentric Visual Query Localization
A unified 2D/3D visual query localization pipeline using SAM-guided parsing, a closed-form correlation filter, and a multiplicative semantic-geometric confidence weighting scheme reports state-of-the-art results on Ego4D-VQ.
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