Non-rigid detailed 3D reconstruction of real world scenes has witnessed great success in recent years. However, most existing methods take the first frame as canonical model and the topological structure of the input scenes are fixed during the reconstruction process, which is an assumption that may not hold in practice for highly non-rigid scenes. Regarding this issue, this work proposes a novel approach to reconstruct non-rigid object with large movement which often results in topological structure change. In this paper, we firstly introduce an adaptive strategy that can effectively identify the most fine-grained scene topology as the canonical model. Such model is then deformed to each depth map, constrained by robust inter-frame correspondences established from object contour and scene flows. After deformation, we further fuse the depth map to the canonical model via a novel adaptive selection scheme, so as to remove spurious noise without smoothing model details. Experimental results show that the proposed approach can effectively handle various input scenes with large movement and generate models with high-fidelity details.

近年来，对真实场景进行非刚性的详细3D重建已取得了巨大的成功。但是，大多数现有方法都将第一帧作为规范模型，并且在重建过程中输入场景的拓扑结构是固定的，这是一个假设，在高度非刚性场景中可能不成立。关于这个问题，这项工作提出了一种新颖的方法来重建运动较大的非刚性物体，这通常会导致拓扑结构的变化。在本文中，我们首先介绍一种自适应策略，该策略可以有效地将最细粒度的场景拓扑识别为规范模型。然后，根据从对象轮廓和场景流建立的稳健的帧间对应关系，将此类模型变形为每个深度图。变形后 我们通过一种新颖的自适应选择方案进一步将深度图融合到规范模型中，从而在不使模型细节变得平滑的情况下消除了杂散噪声。实验结果表明，该方法可以有效地处理各种较大运动的输入场景，并生成高保真细节的模型。