Pose estimation in robotics is often achieved using images from known and purposefully applied markers or fiducials taken by a monocular camera. This low-cost system architecture can provide accurate and precise pose estimation measurements. However, to prevent the restriction of robotic movement and occlusions of features, the fiducial markers are often planar. While numerous planar fiducials exist, the performance of these markers suffers from pose ambiguities and loss of precision under frontal observations. These issues are most prevalent in systems with less-than-ideal specifications such as low-resolution detectors, low field of view optics, far-range measurements etc. To mitigate these issues, encoding markers have been proposed in literature. These markers encode an extra dimension of information in the signal between marker and sensor, thus increasing the robustness of the pose solution. In this work, we provide a survey of these encoding markers and show that existing solutions are complex, require optical elements and are not scalable. Therefore, we present a novel encoding element based on the compound eye of insects such as the Mantis. The encoding element encodes a virtual point in space in its signal without the use of optical elements. The features provided by the encoding element are mathematically equivalent to those of a protrusion. Where existing encoding fiducials require custom software, the projected virtual point can be used with standard pose solving algorithms. The encoding element is simple, can be produced using a consumer 3D printer and is fully scalable. The end-to-end implementation of the encoding element proposed in this work significantly increases the pose estimation performance of existing planar fiducials, enabling robust pose estimation for robotic systems.@en