Abstract
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.
Highlights
In recent years, in the field of animal protection, computer three-dimensional reconstruction methods are more and more used in the study of animal morphology
We found that the accuracy of the 3D giant panda model based on the single image is related to the results of 3D giant panda pose modeling based on the skinned multianimal linear model (SMAL) model
Through the analysis of giant panda video image sequence (GPVS), we prove that it has the longrange–dependent characteristics [17, 18]
Summary
In the field of animal protection, computer three-dimensional reconstruction methods are more and more used in the study of animal morphology. Through flexible and diversified forms such as rapid three-dimensional reconstruction and three-dimensional display, people can have a more intuitive and comprehensive understanding of the species of giant panda and further enhance people’s awareness of animal protection. In 2013, Grag, Ravi, and other scholars used a variant algorithm for dense 3D reconstruction of nonrigid surfaces from monocular video sequences [3], which formulate the nonrigid structure of NRSFM into a global variational energy minimization problem This method can reconstruct highly deformed smooth surfaces. Because the temporal relationship between frames is not considered in the giant panda 3D model of single frame image data, the motion sequence composed of the results of single-frame pose modeling will be uneven and not smooth Such errors are difficult to be automatically corrected in the single-frame algorithm.
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