Depth completion aims to predict dense depth from sparse sensor measurements with corresponding RGB guidance. Existing methods commonly suffer from texture copying and edge blurring due to the explicit fusion of RGB features. To address this, we propose a frequency-guided refinement approach that decouples structure from texture transfer in the frequency domain. Specifically, we decompose RGB features into wavelet sub-bands, and learn content-adaptive kernels that promote smooth propagation for low frequencies while preserving sharp boundaries for high frequencies. Importantly, RGB information serves only as conditioning signals to determine when and how filtering is applied, rather than being directly mixed with depth representations. To further improve robustness, a reliability-aware cross-stage modulation uses encoder features as priors to enhance trustworthy structures and suppress uncertain updates during multi-scale reconstruction. Extensive experiments on benchmark datasets demonstrate that our method generates high-fidelity depth maps with sharp edges and suppressed texture artifacts, achieving state-of-the-art performance.
Wang et al. (Sat,) studied this question.
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