EZ-Therm: Effective Zero-Shot Thermal Depth Completion by Adapting Diffusion Priors

Abstract

For autonomous systems operating in degraded visual environments, robust 3D perception depends on accurate, metric-scale depth prediction. Yet, thermal depth completion—despite being well-suited for such conditions—remains relatively underexplored. Conventional depth completion approaches, which fuse sparse LiDAR with RGB images, perform well under well-lit conditions but break down in low light or adverse conditions due to the inherent limitations of RGB sensing. We present the first diffusion-based zero-shot thermal depth completion framework, extending the capability of an RGB-trained foundation model to the thermal domain. Our approach is twofold: First, to bridge the domain gap, we introduce a Thermal-Aware Multispectral Adaptation (TAMA) that leverages paired RGB-thermal images to enhance the model’s understanding of thermal image. Second, we replace the iterative optimization process with our Thermal-aware Proxy Alignment (TAPA) module, which directly integrates thermal guidance and sparse metric constraints into the diffusion sampling process. Our framework enables reliable, metric-scale depth prediction across diverse thermal scenes. Notably, our zero-shot method demonstrates performance competitive with fully-supervised baselines that require task-specific training, establishing a foundation for robust 3D perception in challenging environments.