14 papers
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…
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…
Grids Often Outperform Implicit Neural Representations at Compressing Dense Signals
Namhoon Kim, Sara Fridovich-Keil
Implicit Neural Representations (INRs) have recently shown impressive results, but their fundamental capacity, implicit biases, and scaling behavior remain poorly understood. We in…
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…