用单步扩散模型实现高精度3D语义场景补全
FlowSSC: Universal Generative Monocular Semantic Scene Completion via One-Step Latent Diffusion
- 将单目图像的语义场景补全转化为条件生成任务，采用三平面隐空间加速推理
- 仅用1步即可完成高质量生成，速度远超传统扩散模型（数百步）
- 可无缝集成现有方法，适合自动驾驶等实时系统应用
从单目RGB图像进行语义场景补全（SSC）是一项基础但极具挑战的任务，因从单一视角推断被遮挡的3D结构存在固有歧义。尽管前馈方法取得进展，但在遮挡区域生成合理细节及保持物体间空间关系方面仍存不足。准确的全局3D生成推理能力对实际应用至关重要。本文提出FlowSSC，首个直接应用于单目语义场景补全的生成框架。该方法将SSC视为条件生成问题，可无缝集成至现有前馈方法中显著提升性能。为实现实时推理而不牺牲质量，引入短路流匹配（Shortcut Flow-matching），在紧凑的三平面隐空间中运行。与需数百步的标准扩散模型不同，本方法通过捷径机制实现单步高保真生成，支持自主系统中的实用部署。在SemanticKITTI上的大量实验表明，FlowSSC达到当前最优性能，显著超越现有基线。
原文摘要 · Abstract (English)
Semantic Scene Completion (SSC) from monocular RGB images is a fundamental yet challenging task due to the inherent ambiguity of inferring occluded 3D geometry from a single view. While feed-forward methods have made progress, they often struggle to generate plausible details in occluded regions and preserve the fundamental spatial relationships of objects. Such accurate generative reasoning capability for the entire 3D space is critical in real-world applications. In this paper, we present FlowSSC, the first generative framework applied directly to monocular semantic scene completion. FlowSSC treats the SSC task as a conditional generation problem and can seamlessly integrate with existing feed-forward SSC methods to significantly boost their performance. To achieve real-time inference without compromising quality, we introduce Shortcut Flow-matching that operates in a compact triplane latent space. Unlike standard diffusion models that require hundreds of steps, our method utilizes a shortcut mechanism to achieve high-fidelity generation in a single step, enabling practical deployment in autonomous systems. Extensive experiments on SemanticKITTI demonstrate that FlowSSC achieves state-of-the-art performance, significantly outperforming existing baselines.
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