XDG：加速视觉消歧

中文摘要 AI 辅助

视觉混叠，又称孪生问题，仍是运动恢复结构（SfM）的核心挑战：视觉相似但物理不同的表面会产生错误的图像匹配，降低重建质量。现有研究通过几何感知的基础模型特征缓解该问题，但在骨干网络上方部署了重型Transformer分类器，导致大规模消歧成本高昂。本文提出XDG，一种为可扩展SfM设计的高效视觉消歧模型。核心观察是3D基础模型已具备视觉消歧所需的跨视图几何推理能力，因此孪生分类应直接适配骨干网络表示，而非在单独的重型解码器中重新学习配对推理。XDG通过轻量LoRA适配器微调Depth Anything 3，并将其相机令牌重新用作紧凑的配对级分类令牌；一个紧凑的MLP头预测候选图像对是否观测到同一3D表面。大量实验表明，XDG在精度与效率间取得良好平衡：在配对和重建基准上与最先进的消歧方法表现相当，且推理速度提升超3倍；在包含数千张图像的单个LaMAR场景中，XDG节省超10小时的视觉消歧处理时间。代码可在该URL获取。

英文摘要

Visual aliasing, also known as the doppelganger problem, remains a key challenge for structure-from-motion (SfM): visually similar but physically distinct surfaces can produce incorrect image matches and degrade reconstruction quality. Previous work mitigates this issue with geometry-aware foundation-model features, but places a heavy transformer classifier on top of the backbone, making large-scale disambiguation expensive. We introduce XDG, an efficient visual disambiguation model designed for scalable SfM. Our key observation is that a 3D foundation model already performs the cross-view geometric reasoning necessary for visual disambiguation, so doppelganger classification should adapt the backbone representation directly rather than relearn pair reasoning in a separate heavy decoder. XDG fine-tunes Depth Anything 3 with lightweight LoRA adapters and repurposes its camera tokens as compact pair-level classification tokens. A compact MLP head predicts whether a candidate image pair observes the same 3D surface. Extensive experiments show that XDG provides a favorable accuracy-efficiency tradeoff: it remains competitive with the state-of-the-art disambiguation method across pairwise and reconstruction benchmarks and delivers more than a 3x inference speedup. On individual LaMAR scenes containing thousands of images, XDG saves more than 10 hours of visual disambiguation processing. Code is available at https://github.com/xtcpete/xdg.

发表机构

• USC Institute for Creative Technologies（南加州大学创意技术研究所）
• University of Southern California（南加州大学）
• SRI International（SRI国际公司）

机构由 AI 辅助整理，请以论文原文为准。