Progressive Structure from Motion

Abstract

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.

Single Image Shape-from-Silhouettes

“…Geometric methods have a strong vitality in industrial applications. The majority of methods based on Structure-from-Motion (SfM) or Simultaneous Localization and Mapping (SLAM) [27] [55] can generate 3D objects from 2D images by using the principle of multiple view geometry. However, classic geometry-based methods are subject to a number of restrictions.…”

An Optimized Tightly-Coupled VIO Design on the Basis of the Fused Point and Line Features for Patrol Robot Navigation

“…The embedded IMU provides the VIO system with an orthogonal 3-axial acceleration and angular rate in the body (robot) coordinate frame. The camera is mounted on the stationary base of the robot, providing the VIO system with sequential image information, by which it estimates the robot pose in the world coordinate frame and which can be further applied to represent and address the structure from motion (SFM) problem [23,24]. The essential part of integrating these two components consists in updating the state variables of the tightly-coupled VIO system as time evolves, so as to efficiently obtain the global optimum solutions of the state variables.…”

What Correspondences Reveal About Unknown Camera and Motion Models?

“…On the one hand, one can only hope to know the correct two-view relationship after trying out all possible models. This is exactly what is done in the context of SfM for the Fundamental matrix and the 2D projective homography as geometric verification [27,25,16]. However, it is not always clear how one should select among the several models for a given problem even after trying out all of them.…”