3-D Rigid Models from Partial Views - Global Factorization

TL;DR

This paper introduces a global factorization approach for reconstructing 3-D rigid models from partial views in video sequences, improving accuracy by globally inferring complete models from all available observations.

Contribution

It proposes a novel global factorization method that infers complete 3-D models directly from all video frames, handling occlusions and reappearances more effectively than previous methods.

Findings

Enhanced 3-D shape accuracy demonstrated on artificial videos

Improved motion estimation accuracy shown on real videos

Method effectively handles occlusions and reappearances

Abstract

The so-called factorization methods recover 3-D rigid structure from motion by factorizing an observation matrix that collects 2-D projections of features. These methods became popular due to their robustness - they use a large number of views, which constrains adequately the solution - and computational simplicity - the large number of unknowns is computed through an SVD, avoiding non-linear optimization. However, they require that all the entries of the observation matrix are known. This is unlikely to happen in practice, due to self-occlusion and limited field of view. Also, when processing long videos, regions that become occluded often appear again later. Current factorization methods process these as new regions, leading to less accurate estimates of 3-D structure. In this paper, we propose a global factorization method that infers complete 3-D models directly from the 2-D projections in the entire set of available video frames. Our method decides whether a region that has become visible is a region that was seen before, or a previously unseen region, in a global way, i.e., by seeking the simplest rigid object that describes well the entire set of observations. This global approach increases significantly the accuracy of the estimates of the 3-D shape of the scene and the 3-D motion of the camera. Experiments with artificial and real videos illustrate the good performance of our method.