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。

基于神经有向距离场的结构光系统深度重建

Depth Reconstruction with Neural Signed Distance Fields in Structured  Light Systems

Fast and accurate depth sensing has long been a significant research
challenge. Event camera, as a device that quickly responds to intensity
changes, provides a new solution for structured light (SL) systems. In this
paper, we introduce Gray code into event-based SL systems for the first time.
Our setup includes an event camera and Digital Light Processing (DLP)
projector, enabling depth estimation through high-speed projection and decoding
of Gray code patterns. By employing spatio-temporal encoding for point
matching, our method is immune to timestamp noise, realizing high-speed depth
estimation without loss of accuracy. The binary nature of events and Gray code
minimizes data redundancy, enabling us to fully utilize sensor bandwidth at
100%. Experimental results show that our approach achieves accuracy comparable
to state-of-the-art scanning methods while surpassing them in data acquisition
speed (up to 41 times improvement) without sacrificing accuracy. Our proposed
approach offers a highly promising solution for ultra-fast, real-time, and
high-precision dense depth estimation. Code and dataset will be publicly
available.

通过将格雷码引入事件式结构光系统，我们采用了一种高速、准确的深度估计方法，充分利用传感器带宽，实现了与最先进的扫描方法相媲美的精确度，并在数据采集速度上超越它们（提高了多达 41 倍），而不牺牲准确性。我们的方法为超高速、实时和高精度的深度估计提供了极有前景的解决方案，代码和数据集将公开提供。