ThermoNeRF: Multimodal Neural Radiance Fields for Thermal Novel View Synthesis

Grunnleggende konsepter

ThermoNeRF proposes a multimodal approach using Neural Radiance Fields for accurate thermal image synthesis and reconstruction.

Sammendrag

1. Introduction to ThermoNeRF:
   • ThermoNeRF addresses the challenges of thermal scene reconstruction by proposing a novel multimodal approach based on Neural Radiance Fields.
2. Related Work:
   • NeRF models have been successful in 3D reconstruction, with extensions to various sensor modalities.
3. Thermal Computer Vision:
   • Traditional methods rely on dense data, while recent studies explore object detection and tracking using infrared sensors.
4. NeRF and Multimodality:
   • NeRF models have been extended to incorporate multiple modalities, showing improved scene representations.
5. Preliminary:
   • NeRF learns an implicit scene representation through MLP networks, mapping 3D positions to color and density values.
6. ThermoNeRF:
   • ThermoNeRF leverages paired RGB and thermal images to learn consistent scene geometries across both modalities.
7. Experiments:
   • ThermoNeRF outperforms baselines in temperature estimation and image quality metrics for both thermal and RGB views.
8. Conclusion:
   • ThermoNeRF offers a significant improvement in temperature estimation accuracy while maintaining high-quality RGB views.

Statistikk

Experimental results validate that ThermoNeRF achieves accurate thermal image synthesis, with an average mean absolute error of 1.5°C.

Sitater

Viktige innsikter hentet fra

ThermoNeRF

by Mariam Hassa... klokken arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12154.pdf

Dypere Spørsmål

How can ThermoNeRF be adapted for use with different frameworks?

ThermoNeRF can be adapted for use with different frameworks by ensuring compatibility and integration with the specific requirements of those frameworks. This adaptation may involve modifying the network architecture, data preprocessing steps, loss functions, or training strategies to align with the new framework's constraints and objectives. For instance, if integrating ThermoNeRF into a real-time application, optimizations for speed and efficiency would be crucial. Additionally, adapting ThermoNeRF to work seamlessly with other neural network architectures or libraries may require adjustments in input/output formats and model interfaces.

What are the practical challenges of collecting paired RGB-thermal images for training?

Collecting paired RGB-thermal images for training poses several practical challenges. One major challenge is ensuring accurate alignment between RGB and thermal modalities during data collection to avoid misalignment issues that could affect model performance. Another challenge is obtaining a diverse dataset that captures various scenes under different lighting conditions, temperatures, and environmental settings to ensure robustness in model generalization. Additionally, maintaining consistency in calibration between RGB and thermal cameras throughout data collection is essential but can be technically demanding.

How does the decoupling of MLPs for RGB and thermal modalities impact the overall performance of ThermoNeRF?

The decoupling of Multi-Layer Perceptrons (MLPs) for RGB and thermal modalities in ThermoNeRF has a significant impact on its overall performance. By using separate MLPs for each modality to estimate color information from RGB inputs independently from temperature information derived from thermal inputs, ThermoNeRF ensures that temperature estimates remain unaffected by color variations present in the scene geometry captured by RGB images. This decoupling enhances accuracy in both temperature estimation as well as reconstruction quality while preserving geometric details across both sensor modalities without contamination or bias introduced by joint optimization within a single MLP.