Thermal is Always Wild: Characterizing and Addressing Challenges in Thermal-Only Novel View Synthesis

• URL: http://arxiv.org/abs/2603.20448v1
• Date: Fri, 20 Mar 2026 19:25:04 GMT
• Title: Thermal is Always Wild: Characterizing and Addressing Challenges in Thermal-Only Novel View Synthesis
• Abstract summary: We introduce a lightweight preprocessing and splatting pipeline that expands usable dynamic range and stabilizes per-frame photometry.<n>Our approach achieves state-of-the-art performance across thermal-only NVS benchmarks, without requiring any dataset-specific tuning.
• Score: 7.16501244435469
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Thermal cameras provide reliable visibility in darkness and adverse conditions, but thermal imagery remains significantly harder to use for novel view synthesis (NVS) than visible-light images. This difficulty stems primarily from two characteristics of affordable thermal sensors. First, thermal images have extremely low dynamic range, which weakens appearance cues and limits the gradients available for optimization. Second, thermal data exhibit rapid frame-to-frame photometric fluctuations together with slow radiometric drift, both of which destabilize correspondence estimation and create high-frequency floater artifacts during view synthesis, particularly when no RGB guidance (beyond camera pose) is available. Guided by these observations, we introduce a lightweight preprocessing and splatting pipeline that expands usable dynamic range and stabilizes per-frame photometry. Our approach achieves state-of-the-art performance across thermal-only NVS benchmarks, without requiring any dataset-specific tuning.

Related papers

• PL-LIT: A LiDAR-Inertial-Thermal SLAM Using Point-Line Features and Thermographic Mapping [39.002163918691465]
  Thermal imaging is resilient to adverse conditions, such as intense illumination, low-light operation, and fog.<n>Most thermal cameras employ automatic gain control (AGC), and thermal images often present low global contrast despite containing informative edge structures.<n>We propose a general LiDAR-Inertial-Thermal SLAM system that accommodates both visible-light and thermal cameras.
  arXiv  Detail & Related papers  (2026-06-28T08:13:43Z)
• Unifying Color and Lightness Correction with View-Adaptive Curve Adjustment for Robust 3D Novel View Synthesis [73.27997579020233]
  We propose Luminance-GS++, a 3DGS-based framework for robust NVS under diverse illumination conditions.<n>Our method combines a globally view-adaptive lightness adjustment with a local pixel-wise residual refinement for precise color correction.
  arXiv  Detail & Related papers  (2026-02-20T16:20:50Z)
• Physics-Constrained Cross-Resolution Enhancement Network for Optics-Guided Thermal UAV Image Super-Resolution [19.782662020080522]
  Existing methods compress optical features to match thermal feature dimensions for cross-modal alignment and fusion.<n>We propose PCNet to achieve cross-resolution mutual enhancement between optical and thermal modalities.<n>We also propose a Physics-Driven Thermal Conduction Module (PDTM) that incorporates two-dimensional heat conduction into optical guidance.
  arXiv  Detail & Related papers  (2026-01-07T02:30:27Z)
• Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark [58.61079960074608]
  Existing infrared image enhancement methods focus on tackling individual degradations.<n>All-in-one enhancement methods, commonly applied to RGB sensors, often demonstrate limited effectiveness.
  arXiv  Detail & Related papers  (2025-10-10T12:55:54Z)
• A Novel Tuning Method for Real-time Multiple-Object Tracking Utilizing Thermal Sensor with Complexity Motion Pattern [7.6016974897939535]
  Multi-Object Tracking in thermal images is essential for surveillance systems.<n>The paper introduces a novel tuning method for pedestrian tracking, specifically designed to handle the complex motion patterns in thermal imagery.
  arXiv  Detail & Related papers  (2025-07-03T08:03:35Z)
• Veta-GS: View-dependent deformable 3D Gaussian Splatting for thermal infrared Novel-view Synthesis [3.1457219084519004]
  3D Gaussian Splatting (3D-GS) based on Thermal Infrared (TIR) imaging has gained attention in novel-view synthesis.<n>We introduce Veta-GS, which leverages a view-dependent deformation field and a Thermal Feature Extractor to capture subtle thermal variations.<n>Our method achieves better performance over existing methods.
  arXiv  Detail & Related papers  (2025-05-25T13:20:45Z)
• Thermal3D-GS: Physics-induced 3D Gaussians for Thermal Infrared Novel-view Synthesis [11.793425521298488]
  This paper introduces a physics-induced 3D Gaussian splatting method named Thermal3D-GS. The first large-scale benchmark dataset for this field named Thermal Infrared Novel-view Synthesis dataset (TI-NSD) is created. The results indicate that our method outperforms the baseline method with a 3.03 dB improvement in PSNR.
  arXiv  Detail & Related papers  (2024-09-12T13:46:53Z)
• Leveraging Thermal Modality to Enhance Reconstruction in Low-Light Conditions [25.14690752484963]
  Neural Radiance Fields (NeRF) accomplishes photo-realistic novel view synthesis by learning the implicit representation of a scene from multi-view images. Existing approaches reconstruct low-light scenes from raw images but struggle to recover texture and boundary details in dark regions. We present Thermal-NeRF, which takes thermal and visible raw images as inputs, to accomplish visible and thermal view synthesis simultaneously.
  arXiv  Detail & Related papers  (2024-03-21T00:35:31Z)
• Does Thermal Really Always Matter for RGB-T Salient Object Detection? [153.17156598262656]
  This paper proposes a network named TNet to solve the RGB-T salient object detection (SOD) task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality.
  arXiv  Detail & Related papers  (2022-10-09T13:50:12Z)
• Thermal to Visible Image Synthesis under Atmospheric Turbulence [67.99407460140263]
  In biometrics and surveillance, thermal imagining modalities are often used to capture images in low-light and nighttime conditions. Such imaging systems often suffer from atmospheric turbulence, which introduces severe blur and deformation artifacts to the captured images. An end-to-end reconstruction method is proposed which can directly transform thermal images into visible-spectrum images.
  arXiv  Detail & Related papers  (2022-04-06T19:47:41Z)
• Maximizing Self-supervision from Thermal Image for Effective Self-supervised Learning of Depth and Ego-motion [78.19156040783061]
  Self-supervised learning of depth and ego-motion from thermal images shows strong robustness and reliability under challenging scenarios. The inherent thermal image properties such as weak contrast, blurry edges, and noise hinder to generate effective self-supervision from thermal images. We propose an effective thermal image mapping method that significantly increases image information, such as overall structure, contrast, and details, while preserving temporal consistency.
  arXiv  Detail & Related papers  (2022-01-12T09:49:24Z)
• TimeLens: Event-based Video Frame Interpolation [54.28139783383213]
  We introduce Time Lens, a novel indicates equal contribution method that leverages the advantages of both synthesis-based and flow-based approaches. We show an up to 5.21 dB improvement in terms of PSNR over state-of-the-art frame-based and event-based methods.
  arXiv  Detail & Related papers  (2021-06-14T10:33:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.