LuminaDepth: Self-Supervised Intrinsic Scene Recovery from Heterogeneous Low-Resolution Sensors via Attention-Guided Neural Fields

LuminaDepth: Self-Supervised Intrinsic Scene Recovery from Heterogeneous Low-Resolution Sensors via Attention-Guided Neural Fields | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 10 April 2026 V1 Latest version Share on LuminaDepth: Self-Supervised Intrinsic Scene Recovery from Heterogeneous Low-Resolution Sensors via Attention-Guided Neural Fields Author : Chen Zihan 0009-0003-6901-0442 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177584886.61507547/v1 64 views 35 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper introduces LuminaDepth, a unified deep learning framework for the joint recovery of dense three-dimensional geometry and intrinsic material properties from sparse, heterogeneous, and lowresolution sensor data. Conventional computer vision pipelines, heavily reliant on high-quality RGB or specialized depth sensors (e.g., Li-DAR), fail in adverse conditions typical of low-power consumer devices, thermal imagers, or medical sensors. LuminaDepth addresses this by formulating a coupled problem of signal enhancement and geometry estimation as an intrinsic decomposition task within a neural field representation. The core innovation is an attention-guided mechanism that modulates multi-scale features to preserve high-frequency details critical for depth disambiguation, alongside a novel neural gain control layer for sensor-agnostic input normalization. We pre-train the model on a large-scale synthetic dataset to learn robust priors for shape and reflectance, enabling effective transfer to real-world sparse data without requiring ground-truth 3D scans. Extensive mathematical formulation and experimental validation across two distinct domains-medical surface topology reconstruction from clinical photos and object detection enhancement in autonomous thermal imaging-demonstrate state-ofthe-art performance. LuminaDepth significantly outperforms monocular depth estimators and traditional image enhancement operators, offering a versatile solution for resource-constrained and non-standard imaging applications. Supplementary Material File (lumina.pdf) Download 473.21 KB Information & Authors Information Version history V1 Version 1 10 April 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords 3d reconstruction attention mechanisms intrinsic image decomposition monocular depth estimation neural fields self-supervised learning sensor fusion thermal imaging Authors Affiliations Chen Zihan 0009-0003-6901-0442 [email protected] Sanjiang University View all articles by this author Metrics & Citations Metrics Article Usage 64 views 35 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Chen Zihan. LuminaDepth: Self-Supervised Intrinsic Scene Recovery from Heterogeneous Low-Resolution Sensors via Attention-Guided Neural Fields. Authorea . 10 April 2026. DOI: https://doi.org/10.22541/au.177584886.61507547/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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