# Computational 3D topographic microscopy from terabytes of data per sample

arXiv: https://arxiv.org/abs/2306.02634 · HTML: https://mlsanity.com/paper/2306.02634/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 5 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Imaging across multiple spatial scales with the multi-camera array microscope](https://mlsanity.com/paper/2212.00027/) (2022)
- [Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second](https://mlsanity.com/paper/2301.08351/) (2023)
- [Mesoscopic photogrammetry with an unstabilized phone camera](https://mlsanity.com/paper/2012.06044/) (2020)
- [Computational 3D microscopy with optical coherence refraction tomography](https://mlsanity.com/paper/2202.11837/) (2022)
- [Multi-modal imaging using a cascaded microscope design](https://mlsanity.com/paper/2208.08875/) (2022)

## Abstract (arXiv)

We present a large-scale computational 3D topographic microscope that enables 6-gigapixel profilometric 3D imaging at micron-scale resolution across >110 cm2 areas over multi-millimeter axial ranges. Our computational microscope, termed STARCAM (Scanning Topographic All-in-focus Reconstruction with a Computational Array Microscope), features a parallelized, 54-camera architecture with 3-axis translation to capture, for each sample of interest, a multi-dimensional, 2.1-terabyte (TB) dataset, consisting of a total of 224,640 9.4-megapixel images. We developed a self-supervised neural network-based algorithm for 3D reconstruction and stitching that jointly estimates an all-in-focus photometric composite and 3D height map across the entire field of view, using multi-view stereo information and image sharpness as a focal metric. The memory-efficient, compressed differentiable representation offered by the neural network effectively enables joint participation of the entire multi-TB dataset during the reconstruction process. To demonstrate the broad utility of our new computational microscope, we applied STARCAM to a variety of decimeter-scale objects, with applications ranging from cultural heritage to industrial inspection.