  In computed tomography, the reconstruction is typically obtained on a voxel
grid. In this work, however, we propose a mesh-based reconstruction method. For
tomographic problems, 3D meshes have mostly been studied to simulate data
acquisition, but not for reconstruction, for which a 3D mesh means the inverse
process of estimating shapes from projections. In this paper, we propose a
differentiable forward model for 3D meshes that bridge the gap between the
forward model for 3D surfaces and optimization. We view the forward projection
as a rendering process, and make it differentiable by extending recent work in
differentiable rendering. We use the proposed forward model to reconstruct 3D
shapes directly from projections. Experimental results for single-object
problems show that the proposed method outperforms traditional voxel-based
methods on noisy simulated data. We also apply the proposed method on electron
tomography images of nanoparticles to demonstrate the applicability of the
method on real data.
