让热成像图自动适配任务需求，提升检测与测距效果。
Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images
- 根据任务动态调整热成像的映射方式，无需人工调参。
- 在目标检测和单目深度估计上均提升性能，计算开销小。
- 适合需多任务兼容的热成像系统，如自动驾驶、安防监控。
热红外（TIR）成像在复杂户外环境中提供可靠感知，但因其14位原始格式导致纹理差、对比度低。传统方法依赖特定任务和温度先验进行色调映射，效果受限。本文提出热变色网络（TCNet），针对14位原始TIR图像实现任务自适应色调映射。同一图像可生成不同任务优化的表示，避免手动重缩放及对场景温度或任务特征的先验依赖。TCNet在目标检测和单目深度估计任务中表现更优，计算开销小，且可模块化集成至多种架构。项目页：https://github.com/donkeymouse/ThermalChameleon
原文摘要 · Abstract (English)
Thermal Infrared (TIR) imaging provides robust perception for navigating in challenging outdoor environments but faces issues with poor texture and low image contrast due to its 14/16-bit format. Conventional methods utilize various tone-mapping methods to enhance contrast and photometric consistency of TIR images, however, the choice of tone-mapping is largely dependent on knowing the task and temperature dependent priors to work well. In this paper, we present Thermal Chameleon Network (TCNet), a task-adaptive tone-mapping approach for RAW 14-bit TIR images. Given the same image, TCNet tone-maps different representations of TIR images tailored for each specific task, eliminating the heuristic image rescaling preprocessing and reliance on the extensive prior knowledge of the scene temperature or task-specific characteristics. TCNet exhibits improved generalization performance across object detection and monocular depth estimation, with minimal computational overhead and modular integration to existing architectures for various tasks. Project Page: https://github.com/donkeymouse/ThermalChameleon
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