Structure from Motion or the sparse 3D reconstruction out of individual photos is a long studied topic in computer vision. Yet none of the existing reconstruction pipelines fully addresses a progressive scenario where images are only getting available during the reconstruction process and intermediate results are delivered to the user. Incremental pipelines are capable of growing a 3D model but often get stuck in local minima due to wrong (binding) decisions taken based on incomplete information. Global pipelines on the other hand need the access to the complete viewgraph and are not capable of delivering intermediate results. In this paper we propose a new reconstruction pipeline working in a progressive manner rather than in a batch processing scheme. The pipeline is able to recover from failed reconstructions in early stages, avoids to take binding decisions, delivers a progressive output and yet maintains the capabilities of existing pipelines. We demonstrate and evaluate our method on diverse challenging public and dedicated datasets including those with highly symmetric structures and compare to the state of the art.

本研究提出了一种新的逐步重建流程，具备增量和全局管道的能力，避免了绑定决策，能够产生渐进的输出，并在具有高度对称结构的难以应对的数据集上进行了评估。

渐进式结构运动