无需相机参数，10秒内从少视角重建高精度3D表面
Surf3R: Rapid Surface Reconstruction from Sparse RGB Views in Seconds
- 多视角联合引导的端到端架构，不依赖相机位姿估计
- 在ScanNet++和Replica上达到最新性能，10秒完成重建
- 适合快速3D建模场景，如移动设备实时重建
现有多视角3D重建方法依赖精确的相机标定与姿态估计，需复杂耗时的预处理，限制实际应用。为此，我们提出Surf3R，一种无需相机姿态估计的端到端前馈方法，可在10秒内完成整个场景的3D表面重建。该方法采用多分支多视角解码结构，多个参考视图协同引导重建过程。通过分支内处理、跨视图注意力及分支间融合，模型有效捕捉互补几何线索。此外，我们引入基于显式3D高斯表示的D-Normal正则化器，将表面法向与其他几何参数耦合优化，显著提升3D一致性与表面细节精度。实验表明，Surf3R在ScanNet++和Replica数据集上多项表面重建指标达领先水平，展现出优异泛化性与效率。
原文摘要 · Abstract (English)
Current multi-view 3D reconstruction methods rely on accurate camera calibration and pose estimation, requiring complex and time-intensive pre-processing that hinders their practical deployment. To address this challenge, we introduce Surf3R, an end-to-end feedforward approach that reconstructs 3D surfaces from sparse views without estimating camera poses and completes an entire scene in under 10 seconds. Our method employs a multi-branch and multi-view decoding architecture in which multiple reference views jointly guide the reconstruction process. Through the proposed branch-wise processing, cross-view attention, and inter-branch fusion, the model effectively captures complementary geometric cues without requiring camera calibration. Moreover, we introduce a D-Normal regularizer based on an explicit 3D Gaussian representation for surface reconstruction. It couples surface normals with other geometric parameters to jointly optimize the 3D geometry, significantly improving 3D consistency and surface detail accuracy. Experimental results demonstrate that Surf3R achieves state-of-the-art performance on multiple surface reconstruction metrics on ScanNet++ and Replica datasets, exhibiting excellent generalization and efficiency.
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