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New arXiv papers survey depth estimation progress and introduce novel diffusion model

By PulseAugur Editorial · [2 sources] ·

Two new arXiv papers explore advancements in monocular depth estimation, a fundamental computer vision task. The first paper provides a comprehensive survey of the field, tracing its evolution from early methods to the impact of foundation models like DINOv3 and their applications in areas such as robotics and autonomous driving. The second paper introduces Lapis, a novel framework utilizing linear attention and pixel-space diffusion for efficient, high-fidelity depth estimation, achieving state-of-the-art accuracy with significantly reduced inference times. AI

IMPACT Advances in monocular depth estimation could improve perception for robots, autonomous vehicles, and AR systems.

RANK_REASON Two arXiv papers on computer vision research.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New arXiv papers survey depth estimation progress and introduce novel diffusion model

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Muxin Liu, Xiaoyang Lyu, Yang-Tian Sun, Yi-Hua Huang, Ziyi Yang, Peng Dai, Xiaojuan Qi ·

    Monocular Depth Estimation from a Single Image: Progress and Opportunities

    arXiv:2609.01172v1 Announce Type: new Abstract: Monocular depth estimation has long stood as a fundamental challenge in computer vision, enabling a wide range of applications including 3D reconstruction, robotics, autonomous driving, and augmented reality. This survey traces the …

  2. 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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