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We propose a novel method for the multi-view reconstruction problem. Surfaces which do not have direct support in the input 3D point cloud and hence need not be photo-consistent but represent real parts of the scene (e.g. low-textured walls, windows, cars) are important for achieving complete reconstructions. We augmented the existing Labatut CGF 2009 method with the ability to cope with these difficult surfaces just by changing the t-edge weights in the construction of surfaces by a minimal s-t cut. Our method uses Visual-Hull to reconstruct the difficult surfaces which are not sampled densely enough by the input 3D point cloud. We demonstrate importance of these surfaces on several real-world data sets. We compare our improvement to our implementation of the Labatut CGF 2009 method and show that our method can considerably better reconstruct difficult surfaces while preserving thin structures and details in the same quality and computational time.

This paper presents a scalable multi-view stereo reconstruction method which can deal with a large number of large unorganized images in affordable time and effort. The computational effort of our technique is a linear function of the surface area of the observed scene which is conveniently discretized to represent sufficient but not excessive detail. Our technique works as a filter on a limited number of images at a time and can thus process arbitrarily large data sets using limited memory. By building reconstructions gradually, we avoid unnecessary processing of data which bring little improvement. In experiments with Middlebury and Strecha's databases, we demonstrate that we achieve results comparable to the state of the art with considerably smaller effort than used by previous methods. We present a large scale experiments in which we processed 294 unorganized images of an outdoor scene and reconstruct its 3D model and 1000 images from the Google Street View Pittsburgh Experimental Data Set 1.

We present a wearable audio-visual capturing system, termed AWEAR 2.0, along with its underlying vision components that allow robust self-localization, multi-body pedestrian tracking, and dense scene reconstruction. Designed as a backpack, the system is aimed at supporting the cognitive abilities of the wearer. In this paper, we focus on the design issues for the hardware platform and on the performance of the current state-of-the-art computer vision methods on the acquired sequences. We describe the calibration procedure of the two omni-directional cameras present in the system as well as a structure-from-motion pipeline that allows for stable multi-body tracking even from rather shaky video sequences thanks to ground plane stabilization. Furthermore, we show how a dense scene reconstruction can be obtained from the data acquired with the platform.