Here you can find the benchmark dataset from the paper:
- K. Pauwels, L. Rubio, and E. Ros, “Real-time model-based articulated object pose detection and tracking with variable rigidity constraints,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, Ohio, 2014, pp. 3994–4001.
The dataset contains three sequences in which the same articulated object is manipulated. The 3D model information of this Kubito-object can be found here: Blender and Wavefront OBJ files.
The following calibration info corresponds to the rectified sequences (note these are slightly different from the Rigid Pose dataset):
focal_length = 583.9102; % (in pixels) baseline = 70.8646; % (in mm) nodal_point_x = 317.9841; % column (in pixels) nodal_point_y = 277.0105; % row (in pixels)
Pixels are square and focal lengths and nodal point are identical in both (rectified) images.
See the following previews to get a better idea of the sequences: