# TeX-NeRF: Neural Radiance Fields from Pseudo-TeX Vision

arXiv: https://arxiv.org/abs/2410.04873 · HTML: https://mlsanity.com/paper/2410.04873/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021)
- [In-Place Scene Labelling and Understanding with Implicit Scene Representation](https://mlsanity.com/paper/2103.15875/) (2021)
- [Vision-Only Robot Navigation in a Neural Radiance World](https://mlsanity.com/paper/2110.00168/) (2021)
- [NeRF-RPN: A general framework for object detection in NeRFs](https://mlsanity.com/paper/2211.11646/) (2022)
- [NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields](https://mlsanity.com/paper/2304.14811/) (2023)
- [Segmenting Objects in Day and Night:Edge-Conditioned CNN for Thermal Image Semantic Segmentation](https://mlsanity.com/paper/1907.10303/) (2019)
- [Novel-View Acoustic Synthesis](https://mlsanity.com/paper/2301.08730/) (2023)
- [MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection](https://mlsanity.com/paper/2308.09421/) (2023)
- [Semantic Ray: Learning a Generalizable Semantic Field with Cross-Reprojection Attention](https://mlsanity.com/paper/2303.13014/) (2023)
- [Cross-Spectral Neural Radiance Fields](https://mlsanity.com/paper/2209.00648/) (2022)
- [Thermal-NeRF: Neural Radiance Fields from an Infrared Camera](https://mlsanity.com/paper/2403.10340/) (2024)
- [LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF](https://mlsanity.com/paper/2209.08498/) (2022)
- [Semantic Segmentation for Thermal Images: A Comparative Survey](https://mlsanity.com/paper/2205.13278/) (2022)
- [Exploring Multi-modal Neural Scene Representations With Applications on Thermal Imaging](https://mlsanity.com/paper/2403.11865/) (2024)
- [Why are thermal images blurry](https://mlsanity.com/paper/2307.15800/) (2023)

## Built on by (most cited first, 1 of 1)

- [Thermal is Always Wild: Characterizing and Addressing Challenges in Thermal-Only Novel View Synthesis](https://mlsanity.com/paper/2603.20448/) (2026)

## Abstract (arXiv)

Neural radiance fields (NeRF) has gained significant attention for its exceptional visual effects. However, most existing NeRF methods reconstruct 3D scenes from RGB images captured by visible light cameras. In practical scenarios like darkness, low light, or bad weather, visible light cameras become ineffective. Therefore, we propose TeX-NeRF, a 3D reconstruction method using only infrared images, which introduces the object material emissivity as a priori, preprocesses the infrared images using Pseudo-TeX vision, and maps the temperatures (T), emissivities (e), and textures (X) of the scene into the saturation (S), hue (H), and value (V) channels of the HSV color space, respectively. Novel view synthesis using the processed images has yielded excellent results. Additionally, we introduce 3D-TeX Datasets, the first dataset comprising infrared images and their corresponding Pseudo-TeX vision images. Experiments demonstrate that our method not only matches the quality of scene reconstruction achieved with high-quality RGB images but also provides accurate temperature estimations for objects in the scene.