3D Unknown View Tomography Via Rotation Invariants
3D Unknown View Tomography Via Rotation Invariants
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DOI：
10.1109/icassp40776.2020.9053170
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Mona Zehni;Shuai Huang;Ivan Dokmani'c;Zhizhen Zhao
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文献类型：
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作者：
Mona Zehni;Shuai Huang;Ivan Dokmani'c;Zhizhen Zhao
在本文中，我们研究了从未知角度的一组2D投影中重建3D点源模型的问题。我们的方法消除了通过从嘈杂投影数据中提取一组旋转不变特征来恢复投影角的需求。从功能中，我们通过约束的非convex优化重建密度映射。我们表明，这些特征具有模型的径向和成对距离的形式的几何解释。我们进一步进行消融研究，以研究各种参数对投影数据估计特征质量的影响。我们的结果展示了所提出的方法在各种噪声状态下重建点源模型的潜力。
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.