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Template-based 3D object tracking still lacks a high-precision benchmark ofreal scenes due to the difficulty of annotating the accurate 3D poses of realmoving video objects without using markers. In this paper, we present amulti-view approach to estimate the accurate 3D poses of real moving objects,and then use binocular data to construct a new benchmark for monoculartextureless 3D object tracking. The proposed method requires no markers, andthe cameras only need to be synchronous, relatively fixed as cross-view andcalibrated. Based on our object-centered model, we jointly optimize the objectpose by minimizing shape re-projection constraints in all views, which greatlyimproves the accuracy compared with the single-view approach, and is even moreaccurate than the depth-based method. Our new benchmark dataset contains 20textureless objects, 22 scenes, 404 video sequences and 126K images captured inreal scenes. The annotation error is guaranteed to be less than 2mm, accordingto both theoretical analysis and validation experiments. We re-evaluate thestate-of-the-art 3D object tracking methods with our dataset, reporting theirperformance ranking in real scenes. Our BCOT benchmark and code can be found athttps://ar3dv.github.io/BCOT-Benchmark/.

BCOT: A Markerless High-Precision 3D Object Tracking Benchmark