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Giant panda 3D reconstruction technology plays an important role in the research of giant panda protection. Through the analysis of giant panda video image sequence (GPVS), we prove that it has the long-range–dependent characteristics. This article proposes an algorithm to accurately reconstruct the giant panda 3D model by using the long-range–dependent characteristics of GPVS. First, the algorithm uses a skinned multi-animal linear model (SMAL) to obtain the initial 3D model of giant panda, and the 3D model of the single-frame giant panda image is reconstructed by controlling shape parameters and attitude parameters; then, we use the coherence information contained in the long-range–dependent characteristics between video sequence images to construct a smooth energy function to correct the error of the 3D model. Through this error, we can judge whether the 3D reconstruction result of the giant panda is consistent with the real structural characteristics of the giant panda. The algorithm solves the problem of low 3D reconstruction accuracy and the problem that 3D reconstruction is easily affected by occlusion or interference. Finally, we realize the accurate reconstruction of the giant panda 3D model.

Giant Panda Video Image Sequence and Application in 3D Reconstruction