Differentiable Radio Frequency Ray Tracing for Millimeter-Wave Sensing
This addresses the problem of limited dataset availability and generalization in millimeter-wave sensing for applications like 3D object characterization and environment mapping, representing a new paradigm rather than an incremental improvement.
The paper tackles the challenge of precise 3D reconstruction from sparse millimeter-wave signals by proposing DiffSBR, a differentiable framework that integrates physics-based simulation with gradient optimization, enabling fine-grained reconstruction even for novel objects unseen by the radar previously.
Millimeter wave (mmWave) sensing is an emerging technology with applications in 3D object characterization and environment mapping. However, realizing precise 3D reconstruction from sparse mmWave signals remains challenging. Existing methods rely on data-driven learning, constrained by dataset availability and difficulty in generalization. We propose DiffSBR, a differentiable framework for mmWave-based 3D reconstruction. DiffSBR incorporates a differentiable ray tracing engine to simulate radar point clouds from virtual 3D models. A gradient-based optimizer refines the model parameters to minimize the discrepancy between simulated and real point clouds. Experiments using various radar hardware validate DiffSBR's capability for fine-grained 3D reconstruction, even for novel objects unseen by the radar previously. By integrating physics-based simulation with gradient optimization, DiffSBR transcends the limitations of data-driven approaches and pioneers a new paradigm for mmWave sensing.