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3D pose estimation is a process of predicting the transformation of an object from a user-defined reference pose, given an image or a 3D scan. It arises in computer vision or robotics where the pose or transformation of an object can be used for alignment of a computer-aided design models, identification, grasping , or manipulation of the object.
Pose estimation problems can be solved in different ways depending on the image sensor configuration, and choice of methodology. Three classes of methodologies can be distinguished: Analytic or geometric methods: Given that the image sensor (camera) is calibrated and the mapping from 3D points in the scene and 2D points in the image is known.
Poser (and Poser Pro) is a figure posing and rendering 3D computer graphics program distributed by Bondware. [2] Poser is optimized for the 3D modeling of human figures.It enables beginners to produce basic animations and digital images, along with the extensive availability of third-party digital 3D models.
MakeHuman Team developed a model that combines different anatomical parameters to transition smoothly from the infant to the elderly, from man to woman and from fat to slim. The initial mesh occupies a middle ground, being neither pronounced masculine, nor pronounced feminine, neither young nor old and having a medium muscular definition.
Following is a list of notable 3D modeling software, computer programs used for developing a mathematical representation of any Three Dimensional surface of objects, ...
A neural radiance field (NeRF) is a method based on deep learning for reconstructing a three-dimensional representation of a scene from two-dimensional images. The NeRF model enables downstream applications of novel view synthesis, scene geometry reconstruction, and obtaining the reflectance properties of the scene.