用合成数据训练脚部三维重建，少视角下效果超群。
FOCUS -- Multi-View Foot Reconstruction From Synthetically Trained Dense Correspondences
- 基于合成数据学习密集对应关系，提升跨视角匹配精度。
- 少视角下重建误差低于0.7毫米，速度比现有方法快数倍。
- 适合需要快速高精度脚部建模的医疗与鞋类设计场景。
从多视角校准图像中进行表面重建是一项挑战性任务，通常需要大量重叠图像。本文聚焦于人体脚部重建，借鉴已有成功方法，从多视角RGB图像中提取丰富的像素级几何线索，并融合生成最终3D模型。提出的新方法FOCUS包含三大贡献：(i) 扩展现有合成脚部数据集SynFoot2，新增与参数化脚部模型FIND的密集对应关系；(ii) 在合成数据上训练的不确定性感知密集对应预测器；(iii) 两种基于密集对应预测的3D表面重建方法：一种受结构光启发，另一种基于使用FIND模型的优化方法。实验表明，该方法在少视角设置下达到当前最优重建质量，在多视角下表现可媲美顶尖水平，且运行速度显著更快。研究团队已公开合成数据集，代码见GitHub链接：https://github.com/OllieBoyne/FOCUS。
原文摘要 · Abstract (English)
Surface reconstruction from multiple, calibrated images is a challenging task - often requiring a large number of collected images with significant overlap. We look at the specific case of human foot reconstruction. As with previous successful foot reconstruction work, we seek to extract rich per-pixel geometry cues from multi-view RGB images, and fuse these into a final 3D object. Our method, FOCUS, tackles this problem with 3 main contributions: (i) SynFoot2, an extension of an existing synthetic foot dataset to include a new data type: dense correspondence with the parameterized foot model FIND; (ii) an uncertainty-aware dense correspondence predictor trained on our synthetic dataset; (iii) two methods for reconstructing a 3D surface from dense correspondence predictions: one inspired by Structure-from-Motion, and one optimization-based using the FIND model. We show that our reconstruction achieves state-of-the-art reconstruction quality in a few-view setting, performing comparably to state-of-the-art when many views are available, and runs substantially faster. We release our synthetic dataset to the research community. Code is available at: https://github.com/OllieBoyne/FOCUS
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。