ROBUSfT：鲁棒实时模板三维重建（Shape-from-Template）C++库
ROBUSfT: Robust Real-Time Shape-from-Template, a C++ Library
AI总结：
针对单目2D视觉跟踪变形物体3D形状的欠约束与实时性难题，提出基于模板的ROBUSfT流程，实现30fps、无需训练、抗遮挡的鲁棒重建，性能优于SOTA，已开源为C++库。
摘要：
仅使用单目2D视觉跟踪变形物体的3D形状是一项具有挑战性的问题。这是因为研究人员需要（i）从2D图像推断3D形状，这是一个严重欠约束的问题，以及（ii）实时实现完整的解决方案流程。该流程通常需要特征检测与匹配、误匹配过滤、3D形状推断和特征跟踪算法。我们提出了ROBUSfT，这是一种基于包含物体静止形状、纹理图和变形规律的模板的传统流程。ROBUSfT可直接使用，支持宽基线，能够处理大变形，速度最高可达30 fps，无需训练，并且对视频帧中的部分遮挡和不连续性具有鲁棒性。在具有挑战性的数据集上，它的性能优于当前最先进的方法。ROBUSfT以公开可用的C++库形式实现，我们在https://github.com/mrshetab/ROBUSfT 提供了使用教程。
英文摘要：
Tracking the 3D shape of a deforming object using only monocular 2D vision is a challenging problem. This is because one should (i) infer the 3D shape from a 2D image, which is a severely underconstrained problem, and (ii) implement the whole solution pipeline in real-time. The pipeline typically requires feature detection and matching, mismatch filtering, 3D shape inference and feature tracking algorithms. We propose ROBUSfT, a conventional pipeline based on a template containing the object's rest shape, texturemap and deformation law. ROBUSfT is ready-to-use, wide-baseline, capable of handling large deformations, fast up to 30 fps, free of training, and robust against partial occlusions and discontinuity in video frames. It outperforms the state-of-the-art methods in challenging datasets. ROBUSfT is implemented as a publicly available C++ library and we provide a tutorial on how to use it in https://github.com/mrshetab/ROBUSfT
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
