Math

Challenge

Critical system logic had to serve an established C++ production library as well as fast-moving research and prototyping workflows. Separate implementations would have created duplicated logic and different sources of truth.

Contribution

Built the initial Python binding layer over the C++/CUDA processing stages, including digitization, frame buffers, registration, and reconstruction. Recorded data could be passed through the same implementations used by scanner services, making individual stages available for research and experiments.

Delivered the Rust reconstruction engine for client evaluation as one library and a C header, with no runtime dependencies beyond the platform's system libraries. The caller supplies decoded frames and approximate poses, starts refinement, and reads back the results.

The interface keeps the caller's geometry consistent: depth enters as measured, and poses enter and leave in the same camera convention. The scanner's internal coordinate shift is declared as camera metadata and handled inside the library. Input buffers can be reused as soon as the push call returns; result handles have explicit release functions.

Failures are part of that interface too. Too few frames produce a clear error before processing starts, and a caught worker panic becomes a failed operation with an error message instead of leaving the caller polling forever. A version handshake, host logging callback, and runnable C example give the client a way to check the integration before introducing its own data.

Outcome

The Python bindings let research use the same C++/CUDA implementation as production. The Rust deliverable gives the client a documented, versioned interface for evaluating reconstruction on its own data, with coordinate conversion and worker management kept inside the library.