用自生成的几何信息修复3D重建误差，提升精度与一致性。
GeoFusionLRM: Geometry-Aware Self-Correction for Consistent 3D Reconstruction
- 利用模型自身预测的法向与深度图反馈修正结构
- 在无需额外监督下实现更清晰的几何形状与一致法向
- 适合追求高保真3D重建的视觉与图形研究者
单图像3D重建使用大型重建模型（LRM）进展迅速，但重建结果常出现几何不一致和细节错位，影响真实感。我们提出GeoFusionLRM，一种基于几何感知的自我修正框架，利用模型自身输出的法向与深度预测来优化结构准确性。与仅依赖输入图像特征的方法不同，GeoFusionLRM通过专用Transformer与融合模块将几何线索反馈回模型，使模型能够纠正错误并强化与条件图像的一致性。该设计在无额外监督或外部信号条件下，提升了重建网格与输入视图之间的对齐程度。大量实验表明，相比现有最先进LRM基线，GeoFusionLRM实现了更锐利的几何结构、更一致的法向分布和更高的重建保真度。
原文摘要 · Abstract (English)
Single-image 3D reconstruction with large reconstruction models (LRMs) has advanced rapidly, yet reconstructions often exhibit geometric inconsistencies and misaligned details that limit fidelity. We introduce GeoFusionLRM, a geometry-aware self-correction framework that leverages the model's own normal and depth predictions to refine structural accuracy. Unlike prior approaches that rely solely on features extracted from the input image, GeoFusionLRM feeds back geometric cues through a dedicated transformer and fusion module, enabling the model to correct errors and enforce consistency with the conditioning image. This design improves the alignment between the reconstructed mesh and the input views without additional supervision or external signals. Extensive experiments demonstrate that GeoFusionLRM achieves sharper geometry, more consistent normals, and higher fidelity than state-of-the-art LRM baselines.
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