  Multi-modal fusion is vital to the success of super-resolution of depth maps.
However, commonly used fusion strategies, such as addition and concatenation,
fall short of effectively bridging the modal gap. As a result, guided image
filtering methods have been introduced to mitigate this issue. Nevertheless, it
is observed that their filter kernels usually encounter significant texture
interference and edge inaccuracy. To tackle these two challenges, we introduce
a Scene Prior Filtering network, SPFNet, which utilizes the priors surface
normal and semantic map from large-scale models. Specifically, we design an
All-in-one Prior Propagation that computes the similarity between multi-modal
scene priors, i.e., RGB, normal, semantic, and depth, to reduce the texture
interference. In addition, we present a One-to-one Prior Embedding that
continuously embeds each single-modal prior into depth using Mutual Guided
Filtering, further alleviating the texture interference while enhancing edges.
Our SPFNet has been extensively evaluated on both real and synthetic datasets,
achieving state-of-the-art performance.
