  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.
