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V
Vsevolod Skorokhodov
Schindler - EPFL Lab
C
Chenghao Xu
Schindler - EPFL Lab
S
Shuo Sun
Schindler - EPFL Lab
O
Olga Fink
Schindler - EPFL Lab
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Founder's Pitch
"Adapt existing visual geometric transformers for enhanced RGB-Thermal 3D reconstruction."
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Why It Matters
This research presents a novel approach to enhancing 3D reconstruction and camera pose estimation by fine-tuning visual geometric transformers for use with RGB and thermal inputs, addressing a gap in existing multimodal applications.
Product Angle
Transform SEAR into a software service for industries requiring reliable 3D reconstruction and mapping in challenging environments, such as security, emergency response, and surveillance.
Disruption
SEAR replaces less effective traditional methods in multimodal 3D reconstruction, particularly in environments with poor visibility where traditional RGB-only solutions fail.
Product Opportunity
The market opportunity lies with industries such as emergency services, military, and security where robust and reliable 3D reconstruction and camera pose estimation are crucial for operation effectiveness.
Use Case Idea
Develop a tool for search and rescue operations to visualize 3D maps in low-light or smoky environments using RGB-Thermal imaging.
Science
The paper focuses on adapting pretrained visual geometry transformers, initially trained on RGB data, to work with multimodal RGB-thermal inputs. It introduces SEAR, a fine-tuning strategy that significantly boosts the performance of 3D reconstruction and camera pose estimation by improving the alignment of RGB and thermal images.
Method & Eval
SEAR was evaluated against state-of-the-art methods using a new RGB-T dataset under varying conditions. It demonstrated significant improvements, such as a 29% increase in AUC@30, and maintained minimal inference overhead when compared to the original RGB-pretrained models.
Caveats
The adaptation strategy might not extend easily to other types of sensors or environments not covered in the dataset. Additionally, reliance on a specific type of transformer architecture may limit broader applicability.
Author Intelligence
Vsevolod Skorokhodov
Schindler - EPFL Lab
Chenghao Xu
Schindler - EPFL Lab
Shuo Sun
Schindler - EPFL Lab
Olga Fink
Schindler - EPFL Lab
Malcolm Mielle
Schindler - EPFL Lab