WiViD: Leveraging Wi-Fi and Vision for Depth Estimation via Multimodal Diffusion

Abstract

Depth estimation is crucial for numerous applications, including autonomous driving, robotic navigation and augmented reality. Existing solutions based on LiDAR and mm Wave technologies are constrained by high deployment costs, while those utilizing monocular vision suffer from limited accuracy. To address these challenges, this paper proposes WiViD, a diffusion-based depth estimation system that leverages commercial Wi-Fi and vision. Diffusion models, with their ability to iteratively refine predictions, offer significant advantages in producing accurate and detailed estimations. We introduce a Multimodal Conditional Diffusion (MMCD) mechanism and design two encoding modules: the Complex-Valued CSI Encoder (CCE) and the Residual Image Encoder (RIE). These components fully exploit the spatio-temporal information inherent in Wi-Fi CSI and enable the effective fusion of Wi-Fi CSI and RGB image data, which results in high-precision and robust depth estimation. Experimental results in real-world scenarios demonstrate that WiViD out-performs state-of-the-art (SOTA) monocular methods, reducing the Absolute Relative Error (ARE) by 67.2 %, highlighting the advantages of WiViD in terms of accuracy and reliability.