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Beyond Best-of-: Prior-Consistent DAPS for Ambiguous Nonlinear Inverse Problems
Merve Kocabas, Benjamin A. Burns, Andreas Geiger +2
Diffusion models have become strong priors for solving inverse problems. However, nonlinear inverse problems such as phase retrieval remain challenging when multiple measurement-co…
LACToS: Local Anomaly Detection via Conditional Typicality of Superpixels
Jihoon Hong, Alireza Kheirandish, Rohan Sanda +1
Local anomaly detection is the task of identifying spatially confined anomalous or out-of-distribution (OOD) regions within an otherwise in-distribution (ID) image. Most existing m…
Sparse Light Field Sampling Improves Casual 3D and 4D Reconstruction
Shamus Li, Ruiming Cao, Laura Waller +2
Many consumer smartphones, stereo cameras, and light field cameras record multiple synchronized viewpoints in a single exposure event. However, novel view synthesis pipelines commo…
PolyMerge: Compressing 3D Gaussian Splats with Polytope Coverings for Provably Safe Resource-Constrained Navigation
Jihoon Hong, Chih-Yuan Chiu, Sara Fridovich-Keil +1
Obstacle avoidance is essential for safe navigation and motion planning. Recent radiance field reconstruction methods enable object detection and modeling with high fidelity, but r…
KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems
Alireza Kheirandish, Jihoon Hong, Sara Fridovich-Keil
Diffusion models have shown promising performance as data-driven priors for computational imaging, as well as some capacity to detect out-of-distribution (OOD) images. However, exi…
Bounding Global and Local Compression Error of Signal Parameterizations
Quang Luong Nhat Nguyen, Sara Fridovich-Keil
Differentiable signal parameterizations such as implicit neural representations (INRs) and hybrid models are increasingly central to computational imaging, yet principled tools for…
When, why, and how do diffusion posterior samplers fail? A finite-sample lens
Benjamin A. Burns, Sara Fridovich-Keil
Diffusion models have excellent capacity to model complex distributions of natural data, which has made them a popular and effective choice for posterior sampling in imaging invers…
3D Field of Junctions: A Noise-Robust, Training-Free Structural Prior for Volumetric Inverse Problems
Narges Moeini, Namhoon Kim, Justin Romberg +1
Volume denoising is a foundational problem in computational imaging, as many 3D imaging inverse problems face high levels of measurement noise. Inspired by the strong 2D image deno…
Perfusion Imaging and Single Material Reconstruction in Polychromatic Photon Counting CT
Namhoon Kim, Ashwin Pananjady, Amir Pourmorteza +1
Background: Perfusion computed tomography (CT) images the dynamics of a contrast agent through the body over time, and is one of the highest X-ray dose scans in medical imaging. Re…