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Diffusion models enhance event-camera depth estimation with eSharp

By PulseAugur Editorial · [1 sources] ·

Researchers have developed eSharp, a new framework for monocular depth estimation that utilizes event cameras to overcome limitations in fast-motion and high-dynamic-range scenes. By repurposing diffusion models, eSharp achieves accurate metric depth with exceptional sharpness. The system addresses the challenge of sparse ground-truth data from LiDAR by employing cross-modal distillation with dense pseudo-labels from a frame camera, outperforming existing event-to-depth methods. AI

IMPACT Enhances depth perception capabilities for systems operating in challenging visual conditions, potentially improving autonomous navigation and robotics.

RANK_REASON The cluster describes a new research paper detailing a novel method for depth estimation using diffusion models and event cameras. [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 →

Diffusion models enhance event-camera depth estimation with eSharp

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The cluster describes a new research paper detailing a novel method for depth estimation using diffusion models and event cameras. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Qiushi Dai, Aniket Dashpute, Yuhao Liu, Rishubh Parihar, Guha Balakrishnan, Ashok Veeraraghavan ·

    eSharp: Diffusion-based Sharp Monocular Metric Event-to-Depth Estimation

    arXiv:2611.08731v1 Announce Type: new Abstract: Depth perception is fundamental for autonomous navigation, industrial inspection, and mixed-reality systems. It often operates under strict constraints on power and form factor. In such cases, monocular depth estimation (MDE) is oft…

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