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
this https URL

EgoCOL 是一种用于开放世界三维物体定位的自我中心相机姿态估计方法，可以在 3D 渲染中高召回和精度地估计自我中心帧的相机姿态，比 Ego4D 基线更准确地估计相机姿态。

EgoCOL：针对开放世界 3D 物体定位的自我中心相机位姿估计 @Ego4D 挑战赛 2023

EgoCOL: Egocentric Camera pose estimation for Open-world 3D object  Localization @Ego4D challenge 2023

This report describes our approach for the EGO4D 2023 Visual Query 2D
Localization Challenge. Our method aims to reduce the number of False Positives
(FP) that occur because of high similarity between the visual crop and the
proposed bounding boxes from the baseline's Region Proposal Network (RPN). Our
method uses a transformer to determine similarity in higher dimensions which is
used as our prior belief. The results are then combined together with the
similarity in lower dimensions from the Siamese Head, acting as our
measurement, to generate a posterior which is then used to determine the final
similarity of the visual crop with the proposed bounding box. Our code is
publicly available $\href{this https URL}{here}$.

本文介绍了我们在 EGO4D 2023 视觉查询二维定位挑战赛中采用的方法。我们使用 Transformer 在更高维度上确定相似性并结合来自 Siamese Head 的低维度相似性生成后验概率，最终确定了视觉裁剪和所建议边界框之间的相似性。