Samuele Salti

Unique signatures of histograms for local surface description

This paper deals with local 3D descriptors for surface matching. First, we categorize existing methods into two classes: Signatures and Histograms. Then, by discussion and experiments alike, we point out the key issues of uniqueness and repeatability of the local reference frame. Based on these observations, we formulate a novel comprehensive proposal for surface representation, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. The latter lays at the intersection between Signatures and Histograms, so as to possibly achieve a better balance between descriptiveness and robustness. Experiments on publicly available datasets as well as on range scans obtained with Spacetime Stereo provide a thorough validation of our proposal.

A combined texture-shape descriptor for enhanced 3D feature matching

Motivated by the increasing availability of 3D sensors capable of delivering both shape and texture information, this paper presents a novel descriptor for feature matching in 3D data enriched with texture. The proposed approach stems from the theory of a recently proposed descriptor for 3D data which relies on shape only, and represents its generalization to the case of multiple cues associated with a 3D mesh. The proposed descriptor, dubbed CSHOT, is demonstrated to notably improve the accuracy of feature matching in challenging object recognition scenarios characterized by the presence of clutter and occlusions.

Performance Evaluation of 3D Keypoint Detectors

In the past few years detection of repeatable and distinctive keypoints on 3D surfaces has been the focus of intense research activity, due on the one hand to the increasing diffusion of low-cost 3D sensors, on the other to the growing importance of applications such as 3D shape retrieval and 3D object recognition. This work aims at contributing to the maturity of this field by a thorough evaluation of several recent 3D keypoint detectors. A categorization of existing methods in two classes, that allows for highlighting their common traits, is proposed, so as to abstract all algorithms to two general structures. Moreover, a comprehensive experimental evaluation is carried out in terms of repeatability, distinctiveness and computational efficiency, based on a vast data corpus characterized by nuisances such as noise, clutter, occlusions and viewpoint changes.

SHOT: Unique signatures of histograms for surface and texture description

Abstract This paper presents a local 3D descriptor for surface matching dubbed SHOT. Our proposal stems from a taxonomy of existing methods which highlights two major approaches, referred to as Signatures and Histograms, inherently emphasizing descriptiveness and robustness respectively. We formulate a comprehensive proposal which encompasses a repeatable local reference frame as well as a 3D descriptor, the latter featuring an hybrid structure between Signatures and Histograms so as to aim at a more favorable balance between descriptive power and robustness. A quite peculiar trait of our method concerns seamless integration of multiple cues within the descriptor to improve distinctiveness, which is particularly relevant nowadays due to the increasing availability of affordable RGB-D sensors which can gather both depth and color information. A thorough experimental evaluation based on datasets acquired with different types of sensors, including a novel RGB-D dataset, vouches that SHOT outperforms state-of-the-art local descriptors in experiments addressing descriptor matching for object recognition, 3D reconstruction and shape retrieval.

Adaptive Appearance Modeling for Video Tracking: Survey and Evaluation

Long-term video tracking is of great importance for many applications in real-world scenarios. A key component for achieving long-term tracking is the trackers capability of updating its internal representation of targets (the appearance model) to changing conditions. Given the rapid but fragmented development of this research area, we propose a unified conceptual framework for appearance model adaptation that enables a principled comparison of different approaches. Moreover, we introduce a novel evaluation methodology that enables simultaneous analysis of tracking accuracy and tracking success, without the need of setting application-dependent thresholds. Based on the proposed framework and this novel evaluation methodology, we conduct an extensive experimental comparison of trackers that perform appearance model adaptation. Theoretical and experimental analyses allow us to identify the most effective approaches as well as to highlight design choices that favor resilience to errors during the update process. We conclude the paper with a list of key open research challenges that have been singled out by means of our experimental comparison.

Unique shape context for 3d data description

The use of robust feature descriptors is now key for many 3D tasks such as 3D object recognition and surface alignment. Many descriptors have been proposed in literature which are based on a non-unique local Reference Frame and hence require the computation of multiple descriptions at each feature points. In this paper we show how to deploy a unique local Reference Frame to improve the accuracy and reduce the memory footprint of the well-known 3D Shape Context descriptor. We validate our proposal by means of an experimental analysis carried out on a large dataset of 3D scenes and addressing an object recognition scenario.

A Performance Evaluation of 3D Keypoint Detectors

Intense research activity on 3D data analysis tasks, such as object recognition and shape retrieval, has recently fostered the proposal of many techniques to perform detection of repeatable and distinctive key points in 3D surfaces. This high number of proposals has not been accompanied yet by a comprehensive comparative evaluation of the methods. Motivated by this, our work proposes a performance evaluation of the state-of-the-art in 3D key point detection, mainly addressing the task of 3D object recognition. The evaluation is carried out by analyzing the performance of several prominent methods in terms of robustness to noise (real and synthetic), presence of clutter, occlusions and point-of-view variations.

Traffic sign detection via interest region extraction

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. HighlightsThe paper presents in detail the design of a Traffic Sign Detection pipeline.Interest regions are an effective tool to feed a Traffic Sign Detection pipeline.A context-aware and a traffic light filter can effectively prune false positives.Our algorithm obtains competitive results on a public benchmark dataset.Our pipeline achieves promising results on a challenging mobile mapping dataset.

On the use of implicit shape models for recognition of object categories in 3D data

The ability of recognizing object categories in 3D data is still an underdeveloped topic. This paper investigates on adopting Implicit Shape Models (ISMs) for 3D categorization, that, differently from current approaches, include also information on the geometrical structure of each object category. ISMs have been originally proposed for recognition and localization of categories in cluttered images. Modifications to allow for a correct deployment for 3D data are discussed. Moreover, we propose modifications to three design points within the structure of a standard ISM to enhance its effectiveness for the categorization of databases entries, either 3D or 2D: namely, codebook size and composition, codeword activation strategy and vote weight strategy. Experimental results on two standard 3D datasets allow us to discuss the positive impact of the proposed modifications as well as to show the performance in recognition accuracy yielded by our approach compared to the state of the art.

A traffic sign detection pipeline based on interest region extraction

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.