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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.
In virtual reality (VR) and augmented reality (AR), a pose tracking system detects the precise pose of head-mounted displays, controllers, other objects or body parts within Euclidean space. Pose tracking is often referred to as 6DOF tracking, for the six degrees of freedom in which the pose is often tracked.
Poses are often stored internally as transformation matrices. [2] [3] The term “pose” is largely synonymous with the term “transform”, but a transform may often include scale, whereas pose does not. [4] [5] In computer vision, the pose of an object is often estimated from camera input by the process of pose estimation. This information ...
The tool is specifically designed for the modeling of virtual 3D human models, with a simple and complete pose system that includes the simulation of muscular movement. The interface is easy to use, with fast and intuitive access to the numerous parameters required in modeling the human form.
Walk cycles can be broken up into four key frames: the forward contact point, the first passing pose, the back contact point, and the second passing pose. Frames that are drawn between these key poses (traditionally known as in-betweens) are either hand-drawn or interpolated using computer software. Key frames of a walk cycle
The simulator is a discrete event simulator (based on the POSE system) which is trace driven and uses POSE's Charm++ base. [1] BIGSIM can simulate both the processing components and the message passing system to provide an overall view of system performance characteristics.
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.
The most important criteria, and also the most commonly used, are pose accuracy (AP) and pose repeatability (RP). Repeatability is particularly important when the robot is moved towards the command positions manually ("Teach-In"). If the robot program is generated by a 3D simulation (off-line programming), absolute accuracy is vital, too. Both ...