Structured light depth sensors work by projecting a codeword pattern, usually made up of NIR light, on a scene and measuring distortions in the light received on an NIR camera to get estimates of the camera-projector disparities. A well-known challenge associated with using structured light technology for depth estimation is its sensitivity to NIR components in the ambient illumination spectrum. While various methodologies are employed to increase the codeword-to-ambient-light ratio – for instance, using narrow-band NIR filters and selecting a spectral band for the NIR laser where the interference from ambient light is expected to be low – structured light setups usually do not work well outdoors under direct sunlight. The standard deviation of shot noise increases as the square root of the ambient-light intensity, reducing the SNR of the received codeword pattern and making the decoding process challenging. One way to improve the SNR of the received structured light pattern is to use codewords of larger spatial support for depth sensing. While large codewords do improve the SNR of the received pattern, the disadvantage is decreased spatial resolution of the estimated disparity field. In this paper, we use a multiscale random field (MSRF) to model the codeword labels and use a Bayesian framework, known as sequential MAP (SMAP) estimation, developed originally for image segmentation, for developing a novel multiscale matched filter for structured light decoding. The proposed algorithm decodes codewords at different scales and merges coarse-to-fine disparity estimates using the SMAP framework. We present experimental results demonstrating that our multiscale filter provides noise-robust decoding of the codeword patterns, while preserving spatial resolution of the decoded disparity maps.