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Lapis diffusion model achieves SOTA depth estimation with linear attention

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Lapis, a novel diffusion model for depth estimation that utilizes linear attention to achieve high-quality results with reduced computational cost. This framework addresses the limitations of existing generative models by employing a coarse-to-fine hierarchy, including a Patch-level Consistency Module for structural coherence and a Pixel-level Refinement Module for sharp geometric boundaries. Lapis demonstrates state-of-the-art accuracy and boundary sharpness across various resolutions, significantly decreasing inference latency compared to previous methods. AI

IMPACT This model offers a more efficient approach to depth estimation, potentially enabling wider adoption in high-resolution applications.

RANK_REASON The cluster contains a research paper detailing a new model and its performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Lapis diffusion model achieves SOTA depth estimation with linear attention

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The cluster contains a research paper detailing a new model and its performance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Bingde Liu, Wu Ran, Jinglei Zhang, Huanhuan Yuan, Chao Ma ·

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

    arXiv:2608.30129v1 Announce Type: new 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 gen…

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