We introduce a novel depth estimation technique for multi-frame structured
light setups using neural implicit representations of 3D space. Our approach
employs a neural signed distance field (SDF), trained through self-supervised
differentiable rendering. Unlike passive vision, where joint estimation of
radiance and geometry fields is necessary, we capitalize on known radiance
fields from projected patterns in structured light systems. This enables
isolated optimization of the geometry field, ensuring convergence and network
efficacy with fixed device positioning. To enhance geometric fidelity, we
incorporate an additional color loss based on object surfaces during training.
Real-world experiments demonstrate our method's superiority in geometric
performance for few-shot scenarios, while achieving comparable results with
increased pattern availability.

我们介绍了一种新颖的基于神经隐式表示法的多帧结构光设置深度估计技术。通过自我监督的可微分渲染训练神经符号距离场（SDF）。与被动视觉不同，我们利用结构光系统中通过投影模式已知的辐射场，实现几何场的独立优化，确保固定设备定位下的收敛性和网络有效性。为了提高几何保真度，我们在训练过程中引入了基于物体表面的额外颜色损失。实际实验表明，我们的方法在少样本情景下在几何性能上具有优越性，并且在增加模式可用性的情况下实现了可比较的结果。