SpINR: Neural Volumetric Reconstruction for FMCW Radars

2025-03-30

AI Summary

Traditional FMCW radar imaging methods (e.g., backprojection) rely on idealized signal models and dense aperture sampling, limiting resolution and generalizability. To address this, we propose the first neural volumetric reconstruction framework tailored for FMCW radar: a differentiable frequency-domain radar forward model explicitly encoding the linear beat-frequency–range relationship; integration of implicit neural representations (INRs) with end-to-end trainable volume rendering; and sparse frequency-domain sampling optimization that preserves physical interpretability while drastically reducing computational cost. Our approach eliminates dependence on dense sampling and idealized assumptions, pioneering the adaptation of neural volume rendering to radar imaging. Extensive experiments on complex scenes demonstrate substantial improvements in reconstruction resolution and geometric accuracy over classical backprojection and state-of-the-art learning-based methods, validating both the feasibility and superiority of neural volumetric reconstruction in the radar domain.

Technology Category

• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Multimodal Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In this paper, we introduce SpINR, a novel framework for volumetric reconstruction using Frequency-Modulated Continuous-Wave (FMCW) radar data. Traditional radar imaging techniques, such as backprojection, often assume ideal signal models and require dense aperture sampling, leading to limitations in resolution and generalization. To address these challenges, SpINR integrates a fully differentiable forward model that operates natively in the frequency domain with implicit neural representations (INRs). This integration leverages the linear relationship between beat frequency and scatterer distance inherent in FMCW radar systems, facilitating more efficient and accurate learning of scene geometry. Additionally, by computing outputs for only the relevant frequency bins, our forward model achieves greater computational efficiency compared to time-domain approaches that process the entire signal before transformation. Through extensive experiments, we demonstrate that SpINR significantly outperforms classical backprojection methods and existing learning-based approaches, achieving higher resolution and more accurate reconstructions of complex scenes. This work represents the first application of neural volumetic reconstruction in the radar domain, offering a promising direction for future research in radar-based imaging and perception systems.

Problem

• Enhances volumetric reconstruction from sparse FMCW radar data
• Overcomes resolution limits of traditional radar imaging techniques
• Introduces neural volumetric reconstruction for radar-based imaging

Innovation

• Differentiable forward model in frequency domain
• Implicit neural representations for scene geometry
• Efficient frequency bin processing for computation