速度こそ正義：単一ステップ拡散によるゼロショット深度補完
Need for Speed: Zero-Shot Depth Completion with Single-Step Diffusion
拡散モデルの事前知識を活用しつつ、推論時の高コストな最適化を排除した単一ステップの深度補完フレームワークを提案。訓練コスト4.5GPU日で高速推論を実現し、屋内・屋外ベンチマークでゼロショット性能と汎化性を示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Jakub Gregorek, Paraskevas Pegios, Nando Metzger, Konrad Schindler, Theodora Kontogianni, Lazaros Nalpantidis
分類: cs.CV, cs.RO
原文アブストラクト
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 shifting computational burden from inference to finetuning, our approach enables efficient and robust 3D perception under real-world latency constraints. Marigold-SSD achieves significantly faster inference with a training cost of only 4.5 GPU days. We evaluate our method across four indoor and two outdoor benchmarks, demonstrating strong cross-domain generalization and zero-shot performance compared to existing depth completion approaches. Our approach significantly narrows the efficiency gap between diffusion-based and discriminative models. Finally, we challenge common evaluation protocols by analyzing performance under varying input sparsity levels. Page: https://dtu-pas.github.io/marigold-ssd/
