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

TL;DR

This paper introduces BCOT, a high-precision, markerless 3D object tracking benchmark created using a multi-view approach to accurately annotate real scene data, facilitating better evaluation of tracking methods.

Contribution

The paper presents a novel multi-view, markerless method for creating a high-precision 3D object tracking benchmark with a large, annotated dataset of real scenes.

Findings

The proposed method achieves less than 2mm annotation error.

The benchmark contains 20 objects, 22 scenes, 404 sequences, and 126K images.

State-of-the-art methods are re-evaluated using the new benchmark.

Abstract

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