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Visual queries 3D localization (VQ3D) is a task in the Ego4D Episodic MemoryBenchmark. Given an egocentric video, the goal is to answer queries of the form"Where did I last see object X?", where the query object X is specified as astatic image, and the answer should be a 3D displacement vector pointing toobject X. However, current techniques use naive ways to estimate the cameraposes of video frames, resulting in a low query with pose (QwP) ratio, thus apoor overall success rate. We design a new pipeline for the challengingegocentric video camera pose estimation problem in our work. Moreover, werevisit the current VQ3D framework and optimize it in terms of performance andefficiency. As a result, we get the top-1 overall success rate of 25.8% on VQ3Dleaderboard, which is two times better than the 8.7% reported by the baseline.

Estimating more camera poses for ego-centric videos is essential for VQ3D