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Marigold-SSD speeds up zero-shot depth completion with single-step diffusion

Researchers have developed Marigold-SSD, a novel single-step diffusion framework for depth completion that significantly reduces inference time. By transferring computational load from testing to fine-tuning, the method achieves efficient and robust 3D perception suitable for real-world latency requirements. Marigold-SSD demonstrates strong zero-shot performance and cross-domain generalization across multiple benchmarks, narrowing the efficiency gap between diffusion-based and discriminative models. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Introduces a faster diffusion model for 3D perception tasks, potentially enabling real-time applications.

RANK_REASON This is a research paper published on arXiv detailing a new diffusion model for depth completion. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

COVERAGE [1]

  1. arXiv cs.CV TIER_1 · Jakub Gregorek, Paraskevas Pegios, Nando Metzger, Konrad Schindler, Theodora Kontogianni, Lazaros Nalpantidis ·

    Need for Speed: Zero-Shot Depth Completion with Single-Step Diffusion

    arXiv:2603.10584v2 Announce Type: replace Abstract: We introduce Marigold-SSD, a single-step, late-fusion depth completion framework that leverages strong diffusion priors while eliminating the costly test-time optimization typically associated with diffusion-based methods. By sh…