Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2611.03396

Extended Field-of-View Passive mmWave Imaging Using Implicit Neural Representations

Sanjaya Herath·Matthew R. Ziemann·Christopher A. Metzler
View original source ↗Read source document ↗Export metadata ↓

Abstract

We introduce a self-supervised learning-based framework for expanding the field-of-view (FoV) of passive real-aperture millimeter wave (mmWave) imaging systems. Our framework exploits aliasing artifacts/grating lobes---which are an inherent part of mmWave image formation---to expand FoV. Specifically, we recognize that removing aliasing artifacts can be cast as an underdetermined deconvolution problem and that in solving this problem, one can expand the system's FoV. We solve this deconvolution problem and expand the system's FoV by enforcing temporal regularity with time-varying Implicit Neural Representations (INRs). We demonstrate the effectiveness of our approach through extensive simulations and real-world experiments with Phase Sensitive Innovation's (PSI) state-of-the-art electro-optical modulation based passive mmWave imaging system.

Explore related subjects

eess.IV

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sanjaya Herath, Matthew R. Ziemann, Christopher A. Metzler. 2026-11-02. Extended Field-of-View Passive mmWave Imaging Using Implicit Neural Representations. https://arxiv.org/abs/2611.03396

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Cyst-X: A Multicenter MRI Benchmark for Pancreatic Cyst Risk Stratification and Cross-Institutional AI

Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic cancer precursors, and guideline-based risk stratification both over-treats indolent lesions and misses progression. We present Cyst-X, a public benchmark of 1,461 T1-weighted (T1W) and T2-weighted (T2W) MRI volumes from 764 patients at seven centers, with expert pancreas masks, risk labels, splits and reference AI risk-prediction models. PanSegNet segmented the pancreas with Dice of 86.34% (T1W) and 89.26% (T2W) on held-out centers. For the primary task, high-risk versus low-risk IPMN, a 3D DenseNet-121 achieved cross-validated AUCs of 74.59% and 73.45%; complementary broad binary and three-class tasks reached 78.67--81.28% and 73.47--75.76%. Because patient images often cannot be pooled across institutions, we also simulated federated learning across the seven centers. On T2W MRI, FedProx was similar to centralized training in center-averaged external AUC (71.00% versus 71.18%) but not in pooled AUC (62.35% versus 77.49%). Radiologist comparisons showed cohort-dependent sensitivity--specificity tradeoffs. Cyst-X supports reproducible method development and identifies within-IPMN risk discrimination and cross-center score comparability as open challenges.

eess.IV↗
arXiv2026

Beyond Visibility: Real-Time Surface Accessibility Fields from Sparse LiDAR

Understanding which surfaces a given tool can physically access is fundamental to robotic interaction, yet 3D perception typically stops at geometric reconstruction or visibility estimation. Visibility does not imply accessibility: a visible point may still be blocked by tool geometry or approach-corridor clearance. We propose the Accessibility Field: a per-point surface accessibility labelling, computed from streaming sparse LiDAR and updated at sensor rate as the platform moves. Our GPU-based method evaluates surface points against precomputed tool-geometry kernels across rotated approach orientations, underpinned by a scan-centric TSDF integration scheme suited to non-repetitive sensors such as the Livox Mid-360. The system is tool-agnostic, requires no prior scene model, and runs in real time on both workstation and Jetson Orin edge hardware. On synthetic primitives, our method achieves F1=90.8%, against 69.8% for a Hidden Point Removal baseline; ablations within our own pipeline give 81.5% replacing the TSDF query with a point-cloud collision check, and 33.9% replacing scan-centric integration with projective, nvblox-style integration. On a mature synthetic Pinus radiata model, the method labels 56.8% of visible pine branch surfaces as physically inaccessible.

eess.IV↗
arXiv2026

Forecasting Brain Tracer Distribution from Pre-Contrast MRI: A Comparison of Direct Regression and Flow Matching

The brain fluid clearance system can be studied with intrathecal contrast-enhanced magnetic resonance imaging (MRI). By injecting a tracer into the spinal canal and performing MRI over 24--48\,h, one can measure how a tracer injected into the cerebrospinal fluid spreads through the brain. As this is an invasive procedure requiring several MRI scans, we ask how much of the later tracer distribution can be predicted from the pre-contrast scan and elapsed time alone. Using 104 training patients (878 MRI volumes), we compared a direct image-to-image regression model with a more complex generative model based on conditional flow matching, and tested both on 23 held-out patients and on 51 patients from a separate cohort with idiopathic normal pressure hydrocephalus (iNPH). Relative to simply copying the pre-contrast scan, the direct model reduced the mean absolute error (intensities normalized to $[0,1]$) from 0.089 to 0.036, about 60\%, in held-out patients and from 0.075 to 0.056, about 25\%, in the iNPH cohort. The reduction held in every sampled elapsed-time interval and brain compartment, and errors remained largest at 20--30\,h and in the ventricles, where enhancement is strongest. Its predicted change maps agreed with the observed tracer enhancement rather than only reproducing anatomy. The generative model was less accurate in both cohorts. A substantial part of the later tracer distribution is therefore predictable from the patient's brain anatomy as seen on pre-contrast MRI, and the added model complexity gave no benefit at this data scale. Whether this accuracy suffices for a given clinical use remains to be established; with prospective validation, such forecasts could help simplify the MRI protocol used to assess brain clearance.

eess.IV↗