Fully in tensor computation manner: one-shot dense 3D structured light and beyond

Tensor computation evolves fast towards a prosperous existence in recent years, e.g. PyTorch. An immediate advantage of using tensor computation is that one does not need to implement low-level parallelism to attain efficient computation, which is of simplicity for both research and application development. The authors began with discovering that a simple manoeuvre ‘tensor shift’ could perform neighbourhood manipulation in very efficient parallel manner. Based on ‘tensor shift’, they derive the tensor version of a renowned correspondence search algorithm: semi-global matching (SGM), which they prefix the name as tensor-SGM. To evaluate their idea, they build-up a novel and practical one-shot structured light 3D acquisition system, which yields state-of-art reconstruction results using off-the-shelf hardware. This is the first fully tensorised 3D reconstruction system published to the authors’ best knowledge, and it opens new possibilities. A major one is, in the same tensorised framework, they solved the pattern interfering problem which hinders multi-structured light systems from working together. This part is marked as ‘beyond’ in this study to avoid confusing the readers the spotlight: the fully tensorised 3D structured light framework.