We present a large-scale computational 3D topographic microscope that enables
6-gigapixel profilometric 3D imaging at micron-scale resolution across $>$110
cm$^2$ 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.

本文提出了一种大规模计算机 3D 地形显微镜，可实现超过 110 平方厘米范围内的微米分辨率的 6 千万像素的三维成像，并且使用基于神经网络的算法重建出整个视场的光度合成和三维高度图，应用于文物保护、工业检测等领域。

计算三维地形显微镜 —— 每个样本数百万兆字节的数据处理

Computational 3D topographic microscopy from terabytes of data per  sample

Imagine an oracle that correctly predicts the outcome of every particle
physics experiment, the products of every chemical reaction, or the function of
every protein. Such an oracle would revolutionize science and technology as we
know them. However, as scientists, we would not be satisfied with the oracle
itself. We want more. We want to comprehend how the oracle conceived these
predictions. This feat, denoted as scientific understanding, has frequently
been recognized as the essential aim of science. Now, the ever-growing power of
computers and artificial intelligence poses one ultimate question: How can
advanced artificial systems contribute to scientific understanding or achieve
it autonomously?
We are convinced that this is not a mere technical question but lies at the
core of science. Therefore, here we set out to answer where we are and where we
can go from here. We first seek advice from the philosophy of science to
understand scientific understanding. Then we review the current state of the
art, both from literature and by collecting dozens of anecdotes from scientists
about how they acquired new conceptual understanding with the help of
computers. Those combined insights help us to define three dimensions of
android-assisted scientific understanding: The android as a I) computational
microscope, II) resource of inspiration and the ultimate, not yet existent III)
agent of understanding. For each dimension, we explain new avenues to push
beyond the status quo and unleash the full power of artificial intelligence's
contribution to the central aim of science. We hope our perspective inspires
and focuses research towards androids that get new scientific understanding and
ultimately bring us closer to true artificial scientists.

本文探讨了人工智能在科学理解方面的贡献，阐述了其作为计算显微镜、灵感资源和未来的理解者三个维度，提出了新的推进人工智能在科学理解中的应用的途径。