Accelerating Point Cloud Computation via Memory in Embedded Structured Light Cameras.

Embedded structured light cameras have been widely applied in various fields. However, due to constraints such as insufficient computing resources, it remains difficult to achieve high-speed structured light point cloud computation. To address this issue, this study proposes a memory-driven computational framework for accelerating point cloud computation. Specifically, the point cloud computation process is precomputed as much as possible and stored in memory in the form of parameters, thereby significantly reducing the computational load during actual point cloud computation. The framework is instantiated in two forms: a low-memory method that minimizes memory footprint at the expense of point cloud stability, and a high-memory method that preserves the nonlinear phase-distance relation via an extensive lookup table. Experimental evaluations demonstrate that the proposed methods achieve comparable accuracy to the conventional method while delivering substantial speedups, and data-format optimizations further reduce required bandwidth. This framework offers a generalizable paradigm for optimizing structured light pipelines, paving the way for enhanced real-time 3D sensing in embedded applications.