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In this paper, we propose an anthropometric parameter measurement method that any customized parameter can be measured online by the pre-selected endpoints on the reconstructed 3D body models of equivariant multi-view images. The method includes 3D body model reconstruction, anthropometric parameter measurement, and parameter modification. In 3D body model reconstruction, we detect and segment the human body from its background and reconstruct a generative 3D body model from the segmented image with deep learning. And then we measure anthropometric parameter on the reconstructed 3D body model of each view. Before parameter measurement, we manually pre-select endpoints associated with all anthropometric parameters on the reconstructed 3D body model since all vertices of the reconstructed body model are ordered. However, the information of a single-view image is insufficient and the measurement result is varied regularly by the view changes. To improve the measurement accuracy, we design a convolutional neural network in the last step which can regress more accurate anthropometric parameters from equivariant multi-view measurements. Experimental results on the representative dataset demonstrate that the proposed method can measure planar and spatial anthropometric parameters automatically with comparable performance.

Neural networks are becoming more popular than traditional methods in stereo matching. The networks can be decomposed into four sub-modules: feature extraction / matching cost computation, cost aggregation, disparity computation / optimization, and disparity refinement. A typical design for the feature extraction networks is that the left and right branches share the same weights. However, the Siamese networks are weak at distinguishing neighboring patches because of the interference of geometric distortion on slanted surfaces. This paper proposes symmetry weight-sharing to improve the feature extraction networks. The geometry of feature extraction and patch comparison has been analyzed, which shows that symmetry weight-sharing can fulfill the geometry on slanted surfaces. A half-translation module is proposed to implement symmetry weight-sharing without additional computational costs. Experiments on the KITTI 2012 and KITTI 2015 datasets show that the symmetry weight-sharing networks have better performance than the weight-sharing networks.