Sining Huang¹, Yukun Song¹, Yixiao Kang¹ and Chang Yu², ¹University of California - Berkeley, USA, ²Northeastern University, USA

In the field of spatial computing, one of the most essential tasks is the pose estimation of 3D objects. While rigid transformations of arbitrary 3D objects are relatively hard to detect due to varying environments introducing factors like insufficient lighting or even occlusion, objects with pre-defined shapes are often easy to track, leveraging geometric constraints. Curved images, with flexible dimensions but a confined shape, are essential shapes often targeted in 3D tracking. Traditionally, proprietary algorithms often require specific curvature measures as the input along with the original flattened images to enable pose estimation for a single image target. In this paper, we propose a pipeline that can detect several logo images simultaneously and only requires the original images as the input, unlocking more effects in downstream fields such as Augmented Reality (AR).

Pose Estimation, 3D Tracking, Curved Images, Geometric Constraints, Spatial Computing, Augmented Reality, Logo detection.