Articulated Pose

Here you can find the benchmark dataset from the paper:

Pauwels, Karl, Rubio, Leonardo, Ros, Eduardo: 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), pp. 3994-4001, Columbus, Ohio, 2014.

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.

The raw sequence data (about 1GB/sequence) can be found here: Wave, Box, Various.

See the following previews to get a better idea of the sequences: