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CMP SfM Web Service is a remote procedure call service operated at the Center of Machine Perception (CMP) of the Czech Technical University in Prague. The majority of available procedures are implementations of Computer Vision methods developed at CMP. The service can be accessed through web page and command line scripting interfaces. This paper presents the service through users' perspective, providing a brief description of the CMP Structure-from-Motion methods and the respective job types. We also explain the technology behind the web service and the most common use cases of the browser interface.

We present a novel method for 3D surface reconstruction from an input cloud of 3D points augmented with visibility information. We observe that it is possible to reconstruct surfaces that do not contain input points. Instead of modeling the surface from input points, we model free space from visibility information of the input points. The complement of the modeled free space is considered full space. The surface occurs at interface between the free and the full space. We show that under certain conditions a part of the full space surrounded by the free space must contain a real object also when the real object does not contain any input points; that is, an occluder reveals itself through occlusion. Our key contribution is the proposal of a new interface classifier that can also detect the occluder interface just from the visibility of input points. We use the interface classifier to modify the state-of-the-art surface reconstruction method so that it gains the ability to reconstruct weakly supported surfaces. We evaluate proposed method on datasets augmented with different levels of noise, undersampling, and amount of outliers. We show that the proposed method outperforms other methods in accuracy and ability to reconstruct weakly supported surfaces.

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.