Efficient and High-Quality Depth Estimation via Pixel-Space Diffusion with Linear Attention

This work presents Lapis, a linear-attention-based pixel-space generative framework that achieves efficient and high-fidelity depthestimation with one-step diffusion. While generative frameworks havesignificantly advanced monocular depth estimation with superior detailfidelity, the O(N 2 ) complexity of standard attention and the multi-stepdenoising process introduce prohibitive computational costs when scal-ing them to high-resolution image applications. Although linear atten-tion and one-step prediction are intuitively viable, directly applying themleads to poor structural consistency, detail loss, and noise. Lapis recti-fies these limitations through a coarse-to-fine hierarchy. Specifically, aPatch-level Consistency Module restores structural coherence by inte-grating semantic and spatial priors. Subsequently, a Pixel-level Refine-ment Module recovers sharp geometric boundaries via skip-connection-based pixel correspondence. Furthermore, to mitigate sampling noise in-herent in one-step diffusion, we leverage the manifold assumption andadopt a direct x-prediction strategy to target the clean data manifold.Extensive evaluations on multiple benchmarks demonstrate that Lapisconsistently achieves state-of-the-art (SOTA) accuracy and boundarysharpness across various resolutions, reducing inference latency by upto 7.6× at 1080P and 10.9× at 1440P resolution compared to previousSOTA generative models.