The paper presents a novel approach called DUSt3R, which aims to address various 3D vision tasks, including 3D reconstruction, without prior information about the scene or cameras. The key contributions of DUSt3R include a unified model that simplifies the traditional reconstruction pipeline, a global alignment procedure for multi-view 3D reconstruction, and promising performance on a range of 3D vision tasks. The approach is designed to handle monocular and multi-view depth benchmarks, as well as multi-view camera pose estimation, achieving state-of-the-art results.

The paper discusses the traditional pipelines for Structure-from-Motion (SfM) and Multi-View Stereo (MVS) and highlights the limitations and vulnerabilities associated with these sequential structures. It also mentions the incorporation of learning-based techniques into the SfM pipeline, such as advanced feature description, image matching, feature-metric refinement, and neural bundle adjustment, which have enhanced the traditional pipeline. However, it emphasizes that the sequential structure of the SfM pipeline persists, making it susceptible to noise and errors in individual components.