AnyThermal：熱知覚のための汎用表現学習に向けて
AnyThermal: Towards Learning Universal Representations for Thermal Perception
RGB-Thermalデータセットと収集プラットフォームを構築し、視覚基盤モデルから蒸留することで、多様な環境とタスクに適用可能な熱画像バックボーンを学習した。
詳しい要約
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著者: Parv Maheshwari, Jay Karhade, Yogesh Chawla, Isaiah Adu, Florian Heisen, Andrew Porco, Andrew Jong, Yifei Liu, Santosh Pitla, Sebastian Scherer, Wenshan Wang
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
We present AnyThermal, a thermal backbone that captures robust task-agnostic thermal features suitable for a variety of tasks such as cross-modal place recognition, thermal segmentation, and monocular depth estimation using thermal images. Existing thermal backbones that follow task-specific training from small-scale data result in utility limited to a specific environment and task. Unlike prior methods, AnyThermal can be used for a wide range of environments (indoor, aerial, off-road, urban) and tasks, all without task-specific training. Our key insight is to distill the feature representations from visual foundation models such as DINOv2 into a thermal encoder using thermal data from these multiple environments. To bridge the diversity gap of the existing RGB-Thermal datasets, we introduce the TartanRGBT platform, the first open-source data collection platform with synced RGB-Thermal image acquisition. We use this payload to collect the TartanRGBT dataset - a diverse and balanced dataset collected in 4 environments. We demonstrate the efficacy of AnyThermal and TartanRGBT, achieving state-of-the-art results with improvements of up to 36% across diverse environments and downstream tasks on existing datasets.
