Abstract Inspired by recent work on robust and fast computation of 3D Local Reference Frames (LRFs), we propose a novel pipeline for coarse registration of 3D point clouds. Key to the method are: (i) the observation that any two corresponding points endowed with an LRF provide a hypothesis on the rigid motion between two views, (ii) the intuition that feature points can be matched based solely on cues directly derived from the computation of the LRF, (iii) a feature detection approach relying on a saliency criterion which captures the ability to establish an LRF repeatably. Unlike related work in literature, we also propose a comprehensive experimental evaluation based on diverse kinds of data (such as those acquired by laser scanners, Kinect and stereo cameras) as well as on quantitative comparison with respect to other methods. We also address the issue of setting the many parameters that characterize coarse registration pipelines fairly and realistically. The experimental evaluation vouches that our method can handle effectively data acquired by different sensors and is remarkably fast.

As integration of depth sensing into mobile devices is likely forthcoming, we investigate on merging appearance and shape information for mobile visual search. Accordingly, we propose an RGB-D search engine architecture that can attain high recognition rates with peculiarly moderate bandwidth requirements. Our experiments include a comparison to the CDVS (Compact Descriptors for Visual Search) pipeline, candidate to become part of the MPEG-7 standard, and contribute to elucidate on the merits and limitations of joint deployment of depth and color in mobile visual search.

This work aims at automatic detection of man-made pole-like structures in scans of urban environments acquired by a 3D sensor mounted on top a moving vehicle. Pole-like structures, such as e.g. road signs and streetlights, are widespread in these environments, and their reliable detection is relevant to applications dealing with autonomous navigation, facility damage detection, city planning and maintenance. Yet, due to the characteristic thin shape, detection of man-made pole-like structures is significantly prone to both noise as well as occlusions and clutter, the latter being pervasive nuisances when scanning urban environments. Our approach is based on a “local” stage, whereby local features are classified and clustered together, followed by a “global” stage aimed at further classification of candidate entities. The proposed pipeline turns out effective in experiments on a standard publicly available dataset as well as on a challenging dataset acquired during the project for validation purposes.

In this paper we present a pipeline for automatic detection of traffic signs in images. The proposed system can deal with high appearance variations, which typically occur in traffic sign recognition applications, especially with strong illumination changes and dramatic scale changes. Unlike most existing systems, our pipeline is based on interest regions extraction rather than a sliding window detection scheme. The proposed approach has been specialized and tested in three variants, each aimed at detecting one of the three categories of Mandatory, Prohibitory and Danger traffic signs. Our proposal has been evaluated experimentally within the German Traffic Sign Detection Benchmark competition.

We investigate on local reference frames (LRF) deployed with 3D descriptors to achieve invariance to objects' pose. We address the task of matching together partial views of surfaces and propose an experimental study on a large corpus of real data which allows for clearly ranking existing LRF proposals based on their repeatability. Then, drawing inspiration from analysis of the experimental findings, we formulate a new proposal which, in particular, peculiarly includes a procedure aimed at estimating a repeatable LRF also at border features, which is very important when matching partial views of surfaces. Experiments show that the new proposal neatly outperforms existing methods in terms of repeatability, is computationally very efficient and provide relevant benefits in practical applications