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.

翻译：毫米波感知是一种新兴技术，在三维物体表征和环境映射方面具有应用前景。然而，从稀疏毫米波信号中实现精确的三维重建仍然面临挑战。现有方法依赖数据驱动学习，受限于数据集可用性和泛化困难。我们提出DiffSBR——一种基于毫米波三维重建的可微框架。DiffSBR集成了可微射线追踪引擎，用于从虚拟三维模型模拟雷达点云。基于梯度的优化器通过最小化模拟点云与真实点云之间的差异来优化模型参数。采用多种雷达硬件的实验验证了DiffSBR在精细三维重建方面的能力，即使是雷达此前未见过的全新物体也能有效重建。通过将基于物理的模拟与梯度优化相结合，DiffSBR突破了数据驱动方法的局限性，开创了毫米波感知的新范式。