We introduce the distributed camera model, a novel model for
Structure-from-Motion (SfM). This model describes image observations in terms
of light rays with ray origins and directions rather than pixels. As such, the
proposed model is capable of describing a single camera or multiple cameras
simultaneously as the collection of all light rays observed. We show how the
distributed camera model is a generalization of the standard camera model and
describe a general formulation and solution to the absolute camera pose problem
that works for standard or distributed cameras. The proposed method computes a
solution that is up to 8 times more efficient and robust to rotation
singularities in comparison with gDLS. Finally, this method is used in an novel
large-scale incremental SfM pipeline where distributed cameras are accurately
and robustly merged together. This pipeline is a direct generalization of
traditional incremental SfM; however, instead of incrementally adding one
camera at a time to grow the reconstruction the reconstruction is grown by
adding a distributed camera. Our pipeline produces highly accurate
reconstructions efficiently by avoiding the need for many bundle adjustment
iterations and is capable of computing a 3D model of Rome from over 15,000
images in just 22 minutes.

本文介绍一种分布式相机模型，是一种新的 Structure-from-Motion (SfM) 模型，并给出了对标准或分布式相机的绝对姿态问题的一般解决方案，以及如何在大规模递增式 SfM 流水线中使用它。与 gDLS 相比，本文提出的方法大约快 8 倍，并且具有更好的鲁棒性，而且该技术能够在仅 22 分钟内从超过 15000 张图像中计算出罗马的 3D 模型。