M ANYeyes are focused upon the objective of quickly obtaining the position and orientation of a target object from a single image or a stream of images. This topic has endured over past and more recent decades (see Besl and Jain [1] or Lowe [2], who study object reconstruction, and Ruel et al. [3], who develop real-time pose estimation techniques) but remains problematic to implement in more generic scenarios. In both air and space environments, scenarios arise that require swift automated guidance with resourceconstrained systems: for example, missile guidance (see Davidovitz and Shinar [4]). Sometimes, the necessity for speed outweighs the desire for precision (seeHmam andKim [5], Agarwal andChaudhuri [6], and Beaulieu [7]). Consistent throughout pose determination methods is the requirement of a sufficiently accurate estimate to begin the process with. The proposed algorithms for International Space Station robotic arm operations from Stieber et al. [8] are dependent on an initial pose estimate. Edge-line correspondence methods are recognized for lacking speed and adaptivity (see Shakunaga [9]). Lowe [2,10] also attempted edge-line correspondence methods to determine the pose of a target, both for static and time-evolving images. This work evolved further with Cropp [11], by solving the edge-line correspondence problem with a RANSAC algorithm (see Fischler and Bolles [12]). The approach suffers from the same constraint: without an initial pose estimate from another system, the pose estimate could take almost 180 times longer than the desired image refresh rate. Heuristics are challenging for an autonomous device but can unlock the ability and potential for more complex and more accurate pose determination techniques that would otherwise take an unacceptably long time. This Note introduces a novel way of filling in the heuristic gap between image acquisition and edge-line correspondence/specific target recognition. Under the assumption that the target’s dimensions are known to an acceptable level of accuracy, it is possible to create a spheroid to model that target. A spheroid is defined as a degenerate ellipsoid: there exists an axis of symmetry for the spheroid. This spheroid describes, up to its axis of symmetry, the target’s position and attitude. From a single image the bounding ellipse of the target’s projection can be extracted, from which the descriptive spheroid can be reconstructed and a pose estimate for the target object can be deduced. Applying a heuristic pose estimation method attempts to create a computational shortcut to more accurate techniques, sacrificing speed for accuracy. In the next section the spheroid reconstruction process is reviewed. An example method of obtaining the spheroid model to describe a target object is described in Sec. III, which minimizes the errors obtained in the approximation. In Sec. IV, this method is used to determine a descriptive spheroid for a proposed satellite of the University of Surrey. In Sec. V, the heuristic theory is applied to this model for varying radial distances. The value of describing a target globally is encapsulated by comparing the reconstruction accuracy with a feature-detection technique, the findings of which are summarized and discussed in Sec. VI.