Papers by Federico Tombari
—Registration is an important step when processing 3D point clouds. Applications for registration... more —Registration is an important step when processing 3D point clouds. Applications for registration range from object modeling and tracking to simultaneous localization and mapping. This article presents the open-source Point Cloud Library (PCL) and the tools therein available for the task of point cloud registration. PCL incorporates methods for the initial alignment of point clouds using a variety of local shape feature descriptors as well as for refining initial alignments using different variants of the well-known Iterative Closest Point (ICP) algorithm. The article provides an overview on registration algorithms, usage examples of their PCL implementations, and tips for their application. Since the choice and parameterization of the right algorithm for a particular type of data is one of the biggest problems in 3D point cloud registration, we present three complete examples of data (and applications) and the respective registration pipeline in PCL. These examples include dense RGB-D point clouds acquired by consumer color and depth cameras, high-resolution laser scans from commercial 3D scanners, and low-resolution sparse point clouds captured by a custom lightweight 3D scanner on a micro aerial vehicle.
Lecture Notes in Computer Science, 2015
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014
Pattern Recognition, 2015
ABSTRACT Mobile mapping systems acquire massive amount of data under uncontrolled conditions and ... more ABSTRACT Mobile mapping systems acquire massive amount of data under uncontrolled conditions and pose new challenges to the development of robust computer vision algorithms. In this work, we show how a combination of solid image analysis and pattern recognition techniques can be used to tackle the problem of traffic sign detection in mobile mapping data. Different from the majority of existing systems, our pipeline is based on interest regions extraction rather than sliding window detection. Thanks to the robustness of local features, the proposed pipeline can withstand great appearance variations, which typically occur in outdoor data, especially dramatic illumination and scale changes. 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, according to the experimental setup of the recent German Traffic Sign Detection Benchmark competition. Besides achieving very good performance in the on-line competition, our proposal has been successfully evaluated on a novel, more challenging dataset of Italian signs, thereby proving its robustness and suitability to automatic analysis of real-world mobile mapping data.
Visual search for mobile devices relies on transmitting wirelessly a compact representation of th... more Visual search for mobile devices relies on transmitting wirelessly a compact representation of the query image, generally in the form of feature descriptors, to a remote server. Descriptors are therefore compressed, so as to reduce bandwidth occupancy and network latency. Given the impressive pace of growth of 3D video technology, we foresee 3D visual search applications for the mobile and the robotic market to become a reality. Accordingly, our work proposes a study on compressed 3D descriptors, a fundamental building block for such prospective applications. Based on analysis of several compression approaches, we develop and assess different schemes to achieve a compact version of a stateof-the-art 3D descriptor. Through experiments on a vast dataset we demonstrate the ability to achieve compression rates as high as 98% with a negligible loss in 3D visual search performance.
Abstract The ability to perceive possible interactions with the environment is a key capability o... more Abstract The ability to perceive possible interactions with the environment is a key capability of task-guided robotic agents. An important subset of possible interactions depends solely on the objects of interest and their position and orientation in the scene. We call these object-based interactions 0-order affordances and divide them among non-hidden and hidden whether the current configuration of an object in the scene renders its affordance directly usable or not. Conversely to other works, we propose that detecting affordances that are ...
We propose a novel method to estimate a unique and repeatable reference frame in the context of 3... more We propose a novel method to estimate a unique and repeatable reference frame in the context of 3D object recognition from a single viewpoint based on global descriptors. We show that the ability of defining a robust reference frame on both model and scene views allows creating descriptive global representations of the object view, with the beneficial effect of enhancing the spatial descriptiveness of the feature and its ability to recognize objects by means of a simple nearest neighbor classifier computed on the descriptor ...
Abstract The literature on local invariant 3D features is growing, also fostered by the advent of... more Abstract The literature on local invariant 3D features is growing, also fostered by the advent of cheap off-the-shelf 3D sensors. Although several recent proposals in the field include both a detector and a descriptor, some of the most successful and used descriptors do not define a companion detector. Moreover, as vouched by the related field of image features, detectors and descriptors defined within the same proposal do not necessarily yield the highest performance when used together. Hence, in this work we investigate on the ...
We propose a novel approach for verifying model hypotheses in cluttered and heavily occluded 3D s... more We propose a novel approach for verifying model hypotheses in cluttered and heavily occluded 3D scenes. Instead of verifying one hypothesis at a time, as done by most state-of-the-art 3D object recognition methods, we determine object and pose instances according to a global optimization stage based on a cost function which encompasses geometrical cues. Peculiar to our approach is the inherent ability to detect significantly occluded objects without increasing the amount of false positives, so that the operating point of the object ...
Surveillance is one of the most promising applications for wireless sensor networks, stimulated b... more Surveillance is one of the most promising applications for wireless sensor networks, stimulated by a confluence of simultaneous advances in key disciplines: computer vision, image sensors, embedded computing, energy harvesting, and sensor networks. However, computer vision typically requires notable amounts of computing performance, a considerable memory footprint and high power consumption. Thus, wireless smart cameras pose a challenge to current
This paper proposes an effective algorithm for recognizing objects and accurately estimating thei... more This paper proposes an effective algorithm for recognizing objects and accurately estimating their 6DOF pose in scenes acquired by a RGB-D sensor. The proposed method is based on a combination of different recognition pipelines, each exploiting the data in a diverse manner and generating object hypotheses that are ultimately fused together in an Hypothesis Verification stage that globally enforces geometrical consistency between model hypotheses and the scene. Such a scheme boosts the overall recognition performance as it enhances the strength of the different recognition pipelines while diminishing the impact of their specific weaknesses. The proposed method outperforms the state-of-the-art on two challenging benchmark datasets for object recognition comprising 35 object models and, respectively, 176 and 353 scenes.
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Papers by Federico Tombari