Robust multi-camera calibration is a fundamental task for all multi-view camera systems, leveraging discreet camera model fitting from sparse target observations. Stereo systems, photogrammetry and light-field arrays have all demonstrated the need for geometrically consistent calibrations to achieve higherlevels of sub-pixel localization accuracy for improved depth estimation. This work presents a calibration target that leverages multi-directional features to achieve improved dense calibrations of camera systems. We begin by presenting a 2D target that uses an encoded feature set, each with 12 bits of uniqueness for flexible patterning and easy identification. These features combine orthogonal sets of straight and circular binary edges, along with Gaussian peaks. Our proposed feature extraction algorithm uses steerable filters for edge localization, and an ellipsoidal peak fitting for the circle center estimation. Feature uniqueness is used for associativity across views, which is combined into a 3D pose graph for nonlinear optimization. Existing camera models are leveraged for intrinsic and extrinsic estimates, demonstrating a reduction in mean re-projection error of for stereo calibration from 0.2 pixels to 0.01 pixels when using a traditional checkerboard and the proposed target respectively.