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

• URL: http://arxiv.org/abs/2608.30129v1
• Date: Mon, 31 Aug 2026 01:27:50 GMT
• Title: Efficient and High-Quality Depth Estimation via Pixel-Space Diffusion with Linear Attention
• Abstract summary: Lapis is a generative framework that achieves efficient and high-fidelity depth estimation with one-step diffusion.<n>It rectifies these limitations through a coarse-to-fine hierarchy.<n>It consistently achieves state-of-the-art (SOTA) accuracy and boundary sharpness across various resolutions.
• Score: 10.565261122171906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents $\textbf{Lapis}$, a $\textbf{l}$inear-$\textbf{a}$ttention-based $\textbf{pi}$xel-$\textbf{s}$pace generative framework that achieves efficient and high-fidelity depth estimation with one-step diffusion. While generative frameworks have significantly advanced monocular depth estimation with superior detail fidelity, the $\mathcal{O}(N^2)$ complexity of standard attention and the multi-step denoising process introduce prohibitive computational costs when scaling them to high-resolution image applications. Although linear attention and one-step prediction are intuitively viable, directly applying them leads to poor structural consistency, detail loss, and noise. Lapis rectifies these limitations through a coarse-to-fine hierarchy. Specifically, a Patch-level Consistency Module restores structural coherence by integrating semantic and spatial priors. Subsequently, a Pixel-level Refinement Module recovers sharp geometric boundaries via skip-connection-based pixel correspondence. Furthermore, to mitigate sampling noise inherent in one-step diffusion, we leverage the manifold assumption and adopt a direct $\mathbf{x}$-prediction strategy to target the clean data manifold. Extensive evaluations on multiple benchmarks demonstrate that Lapis consistently achieves state-of-the-art (SOTA) accuracy and boundary sharpness across various resolutions, reducing inference latency by up to 7.6$\times$ at 1080P and 10.9$\times$ at 1440P resolution compared to previous SOTA generative models.

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