# Passive LWIR Hyperspectral Ranging via Transmittance Extraction and Distance Alignment

arXiv: https://arxiv.org/abs/2609.28967 · HTML: https://mlsanity.com/paper/2609.28967/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 8 of 8)

- [Learning Guided Convolutional Network for Depth Completion](https://mlsanity.com/paper/1908.01238/) (2019)
- [High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion](https://mlsanity.com/paper/2104.01530/) (2021)
- [Why are thermal images blurry](https://mlsanity.com/paper/2307.15800/) (2023)
- [Adaptive LiDAR Sampling and Depth Completion using Ensemble Variance](https://mlsanity.com/paper/2007.13834/) (2020)
- [Absorption-Based, Passive Range Imaging from Hyperspectral Thermal Measurements](https://mlsanity.com/paper/2308.05818/) (2023)
- [Fast and Accurate Depth Estimation from Sparse Light Fields](https://mlsanity.com/paper/1812.06856/) (2018)
- [HADAR-Based Thermal Infrared Hyperspectral Image Restoration](https://mlsanity.com/paper/2605.13664/) (2026)
- [Ozone Cues Mitigate Reflected Downwelling Radiance in LWIR Absorption-Based Ranging](https://mlsanity.com/paper/2603.00273/) (2026)

## Abstract (arXiv)

Passive long-wave infrared (LWIR) hyperspectral ranging enables distance estimation in low-light and nighttime scenes by exploiting atmospheric absorption features in thermal radiance received through the atmosphere.Joint estimation of temperature, emissivity, and distance is computationally expensive. Reference-range joint inversion also uses a distance-invariant effective attenuation coefficient, which can bias range estimates.We introduce transmittance extraction and distance alignment (TEDA), which decouples range estimation from temperature--emissivity inversion. In the first stage, a baseline estimator with a data-fidelity term invariant to the known absorption direction yields two closed-form smoothing branches for the slowly varying thermal continuum. An observation-derived gate combines the branches, and subtracting the blended baseline in the log domain recovers atmospheric transmittance. The second stage estimates range by matching the recovered transmittance to sensor-domain transmittance models recomputed for each candidate distance. Monte Carlo simulations show that TEDA effectively reduces the ranging bias caused by the distance-invariant attenuation coefficient approximation. In a measured scene, TEDA's mean range estimates are closer to the LiDAR medians than those of reference-range joint inversion in both evaluated patches. TEDA processes a complete 256×256 region of interest in 8.19~s versus 159.47~s for reference-range joint inversion, an approximately 20-fold speedup.