STARCAM (Scanning Topographic All-in-focus Reconstruction with a Computational Array Microscope) is a new computational 3D microscopy approach that enables scalable multi-gigapixel 3D topographic reconstruction over >110 cm2 lateral fields of view (FOVs) and multi-mm axial ranges at micron-scale resolution. STARCAM is a direct extension of 3D-RAPID (https://github.com/kevinczhou/3D-RAPID), combining a parallelized 54-camera architecture and 3-axis sample scanning. From the resulting multi-terabyte-per-sample datasets, STARCAM reconstructs and stitches a 6-gigapixel, all-in-focus gigamosaic along with a coregistered 3D height map, using both parallax and sharpness information from the overlapped FOVs and z-stacks. Like 3D-RAPID, STARCAM trains a convolutional neural network (CNN) to map from the raw data to the 3D height maps via self supervision. This repository provides the Python code for performing these large-scale reconstructions.
For more details, see our accompanying paper here (or our arXiv preprint). See also the repositories for 3D-RAPID and smartphone photogrammetry, which STARCAM extends.
Due to the exceedingly large sizes of the datasets (up to 2.1 TB/sample), they are not publicly available at this time -- please contact us. For best performance, the data should be stored on a storage device with fast sustained read speeds (e.g., NVMe drives), since the data will be streamed as random patches during training.
We used the same environment as for 3D-RAPID: https://github.com/kevinczhou/3D-RAPID?tab=readme-ov-file#setting-up-your-environment
The patch and batch sizes were chosen to fit on a 24-GB GPU. Your CPU should ideally have at least 256 GB of RAM.
You will only need to directly interact with the two Jupyter notebooks: training.ipynb, followed by gigamosaic_inference.ipynb.