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Benjamin Shields

2 accepted papers

2026

$\texttt{PRISM}$:A 3D Probabilistic Neural Representation for Interpretable Shape Modeling

ICML 2026poster

Understanding how anatomical shapes evolve in response to developmental covariates—and quantifying their spatially varying uncertainties—is critical in healthcare research. Existing approaches typically rely on global time-warping formulations that ignore spatially heterogeneous dynamics. We introdu…

Cited by 0SourceScholar
2024

$\texttt{NAISR}$: A 3D Neural Additive Model for Interpretable Shape Representation

ICLR 2024spotlight

Deep implicit functions (DIFs) have emerged as a powerful paradigm for many computer vision tasks such as 3D shape reconstruction, generation, registration, completion, editing, and understanding. However, given a set of 3D shapes with associated covariates there is at present no shape representatio…