SIFT-Realistic Rendering

Dominik Sibbing, Torsten Sattler, Bastian Leibe, Leif Kobbelt
Proceedings of Three-dimensional Vision 2013 (3DV 2013), Conference Publishing Services (CPS), IEEE Computer Society Press, Los Alamitos, California.

3D localization approaches establish correspondences between points in a query image and a 3D point cloud reconstruction of the environment. Traditionally, the database models are created from photographs using Structure-from-Motion (SfM) techniques, which requires large collections of densely sampled images. In this paper, we address the question how point cloud data from terrestrial laser scanners can be used instead to significantly reduce the data collection effort and enable more scalable localization.

The key change here is that, in contrast to SfM points, laser-scanned 3D points are not automatically associated with local image features that could be matched to query image features. In order to make this data usable for image-based localization, we explore how point cloud rendering techniques can be leveraged to create virtual views from which database features can be extracted that match real image-based features as closely as possible. We propose different rendering techniques for this task, experimentally quantify how they affect feature repeatability, and demonstrate their benefit for image-based localization.

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