Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations - small sensor size, compact lenses and the lack of specific hardware, - impede them to achieve the quality results of DSLR cameras. In this work we present an end-to-end deep learning approach that bridges this gap by translating ordinary photos into DSLR-quality images. We propose learning the translation function using a residual convolutional neural network that improves both color rendition and image sharpness. Since the standard mean squared loss is not well suited for measuring perceptual image quality, we introduce a composite perceptual error function that combines content, color and texture losses. The first two losses are defined analytically, while the texture loss is learned in an adversarial fashion. We also present DPED, a large-scale dataset that consists of real photos captured from three different phones and one high-end reflex camera. Our quantitative and qualitative assessments reveal that the enhanced image quality is comparable to that of DSLR-taken photos, while the methodology is generalized to any type of digital camera.

In urban environments the most interesting and effective factors for localization and navigation are landmark buildings. This paper proposes a novel method to detect such buildings that stand out, i.e. would be given the status of 'landmark'. The method works in a fully unsupervised way, i.e. it can be applied to different cities without requiring annotation. First, salient points are detected, based on the analysis of their features as well as those found in their spatial neighborhood. Second, learning refines the points by finding connected landmark components and training a classifier to distinguish these from common building components. Third, landmark components are aggregated into complete landmark buildings. Experiments on city-scale point clouds show the viability and efficiency of our approach on various tasks.

Decomposing 3D building models into architectural elements is an essential step in understanding their 3D structure. Although we focus on landmark buildings, our approach generalizes to arbitrary 3D objects. We formulate the decomposition as a multi-label optimization that identifies individual elements of a landmark. This allows our system to cope with noisy, incomplete, outlier-contaminated 3D point clouds. We detect three types of structural cues, namely dominant mirror symmetries, rotational symmetries, and polylines capturing free-form shapes of the landmark not explained by symmetry. Combining these cues enables modeling the variability present in complex 3D models, and robustly decomposing them into architectural structural elements. Our architectural decomposition facilitates significant 3D model compression and shape-specific modeling.

Image-to-image feature matching is the single most restrictive time bottleneck in any matching pipeline. We propose two methods for improving the speed and quality by employing semantic scene segmentation. First, we introduce a way of capturing semantic scene context of a key point into a compact description. Second, we propose to learn correct match ability of descriptors from these semantic contexts. Finally, we further reduce the complexity of matching to only a pre-computed set of semantically close key points. All methods can be used independently and in the evaluation we show combinations for maximum speed benefits. Overall, our proposed methods outperform all baselines and provide significant improvements in accuracy and an order of magnitude faster key point matching.