Over the past decade, vendors across fields ranging from entertainment to retail to architecture have increasingly been shifting everyday consumable media from the 2D plane to the 3D world. Applications in these fields such as augmented/virtual reality, online shopping, and indoor scene reconstruction all have one crucial problem that needs to be addressed: the task of acquiring high-quality 3D models. Currently, designing novel 3D content involves a substantial human component; indeed, most 3D models were created via modeling software, eg, 3DS Max, Maya, by a human with vast expertise. Although several research communities have explored automated 3D scanning over the course of several decades, the lack of an out-of-the-box solution has precluded this field from permeating the industry. Prior approaches to 3D scanning can largely be divided into image-based reconstruction (IBR) and active reconstruction (AR). While IBR methods produce reconstructions by taking in as input a collection of calibrated RGB images, AR methods actively project patterns onto observed scenes during reconstruction. IBR and AR techniques each have advantages and pitfalls. State of the art methods in 3D scanning typically fall purely within IBR or AR.