Conventional RGB stereo matching suffers from ambiguities caused by metamerism and weak textures. We demonstrate that implicitly recovered spectral information can enhance depth estimation without requiring spectral sensors at deployment. A reconstruction-to-matching architecture maps RGB inputs to a latent spectral space, injecting material priors into stereo matching. To address data scarcity, we develop a co-aperture system combining a liquid crystal tunable filter with Scheimpflug LiDAR for pixel-aligned multimodal acquisition, and propose a “Measure-and-Complete” strategy using sparse LiDAR to generate dense pseudo-ground truth. Experiments show 4.34% reduction in endpoint error, validating the effectiveness of spectral priors for geometric perception.