  In this paper, we study the problem of reconstructing a 3D point source model
from a set of 2D projections at unknown view angles. Our method obviates the
need to recover the projection angles by extracting a set of rotation-invariant
features from the noisy projection data. From the features, we reconstruct the
density map through a constrained nonconvex optimization. We show that the
features have geometric interpretations in the form of radial and pairwise
distances of the model. We further perform an ablation study to examine the
effect of various parameters on the quality of the estimated features from the
projection data. Our results showcase the potential of the proposed method in
reconstructing point source models in various noise regimes.
