  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.
