title: ThermalNeRF: Thermal Radiance Fields
summary: Thermal imaging has a variety of applications, from agricultural monitoring
  to building inspection to imaging under poor visibility, such as in low light,
  fog, and rain. However, reconstructing thermal scenes in 3D presents several
  challenges due to the comparatively lower resolution and limited features
  present in long-wave infrared (LWIR) images. To overcome these challenges, we
  propose a unified framework for scene reconstruction from a set of LWIR and RGB
  images, using a multispectral radiance field to represent a scene viewed by
  both visible and infrared cameras, thus leveraging information across both
  spectra. We calibrate the RGB and infrared cameras with respect to each other,
  as a preprocessing step using a simple calibration target. We demonstrate our
  method on real-world sets of RGB and LWIR photographs captured from a handheld
  thermal camera, showing the effectiveness of our method at scene representation
  across the visible and infrared spectra. We show that our method is capable of
  thermal super-resolution, as well as visually removing obstacles to reveal
  objects that are occluded in either the RGB or thermal channels. Please see
  https://yvette256.github.io/thermalnerf for video results as well as our code
  and dataset release.
paper_authors._id: 669f3dcaece12f050c2574ba
paper_authors.name: Yvette Y. Lin
paper_authors._id: 669f3dcaece12f050c2574bb
paper_authors.name: Xin-Yi Pan
paper_authors._id: 669f3dcaece12f050c2574bc
paper_authors.name: Sara Fridovich-Keil
paper_authors._id: 669f3dcaece12f050c2574bd
paper_authors.name: Gordon Wetzstein
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-23T12:52:09.832Z
paper_authors._id: 6694e583ac96ca2c17131505
paper_authors.avatarUrl: /avatars/6e7a31f257e36cf301da6f879dc0a122.svg
paper_authors.fullname: Gordon Wetzstein
paper_authors.type: user
paper_authors.user: wetzste1
paper_title: ThermalNeRF: Thermal Radiance Fields
paper_summary: Thermal imaging has a variety of applications, from agricultural monitoring
  to building inspection to imaging under poor visibility, such as in low light,
  fog, and rain. However, reconstructing thermal scenes in 3D presents several
  challenges due to the comparatively lower resolution and limited features
  present in long-wave infrared (LWIR) images. To overcome these challenges, we
  propose a unified framework for scene reconstruction from a set of LWIR and RGB
  images, using a multispectral radiance field to represent a scene viewed by
  both visible and infrared cameras, thus leveraging information across both
  spectra. We calibrate the RGB and infrared cameras with respect to each other,
  as a preprocessing step using a simple calibration target. We demonstrate our
  method on real-world sets of RGB and LWIR photographs captured from a handheld
  thermal camera, showing the effectiveness of our method at scene representation
  across the visible and infrared spectra. We show that our method is capable of
  thermal super-resolution, as well as visually removing obstacles to reveal
  objects that are occluded in either the RGB or thermal channels. Please see
  https://yvette256.github.io/thermalnerf for video results as well as our code
  and dataset release.
paper_ai_summary: A framework using multispectral radiance fields and calibrate cameras for effective 3D reconstruction and thermal super-resolution from RGB and LWIR images.
paper_ai_keywords: multispectral radiance field