用通用模型实现遥感建筑3D重建，效果优于传统方法。
SAM 3D for 3D Object Reconstruction from Remote Sensing Images
- 用SAM 3D通用模型做单目遥感建筑重建，无需定制架构。
- 相比TRELLIS，屋顶结构更连贯、边界更清晰，FID和CMMD指标更优。
- 可扩展至城市场景重建，适合城市建模与遥感应用研究者。
从遥感影像中进行单目3D建筑重建对可扩展的城市建模至关重要，但现有方法常需特定架构并依赖大量监督。本文首次系统评估了SAM 3D——一种通用图像到3D的基础模型在单目遥感建筑重建中的表现。我们在纽约城市数据集（NYC Urban Dataset）样本上对比SAM 3D与TRELLIS，采用弗雷谢特初始距离（FID）和基于CLIP的最大均值差异（CMMD）作为评估指标。实验表明，SAM 3D生成的屋顶几何更连贯、边界更锐利。我们进一步通过“分割-重建-组合”流程将SAM 3D扩展至城市场景重建，展示其在城市建模中的潜力。同时分析了实际局限性，并讨论未来研究方向。这些发现为在城市3D重建中部署基础模型提供了实践指导，并推动未来融入场景级结构先验。
原文摘要 · Abstract (English)
Monocular 3D building reconstruction from remote sensing imagery is essential for scalable urban modeling, yet existing methods often require task-specific architectures and intensive supervision. This paper presents the first systematic evaluation of SAM 3D, a general-purpose image-to-3D foundation model, for monocular remote sensing building reconstruction. We benchmark SAM 3D against TRELLIS on samples from the NYC Urban Dataset, employing Frechet Inception Distance (FID) and CLIP-based Maximum Mean Discrepancy (CMMD) as evaluation metrics. Experimental results demonstrate that SAM 3D produces more coherent roof geometry and sharper boundaries compared to TRELLIS. We further extend SAM 3D to urban scene reconstruction through a segment-reconstruct-compose pipeline, demonstrating its potential for urban scene modeling. We also analyze practical limitations and discuss future research directions. These findings provide practical guidance for deploying foundation models in urban 3D reconstruction and motivate future integration of scene-level structural priors.
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