Efficient and High-Quality Depth Estimation via Pixel-Space Diffusion with Linear Attention
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 rec...
| Authors | Bingde Liu, Wu Ran, Jinglei Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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