Creating and Recognizing 3D Objects

This thesis aims at investigating on 3D Computer Vision, a research topic which is gathering even increasing attention thanks to the more and more widespread availability of affordable 3D visual sensor, such as, in particular consumer grade RGB-D cameras. The contribution of this dissertation is twofold. First, the work addresses how to compactly represent the content of images acquired with RGB-D cameras. Second, the thesis focuses on 3D Reconstruction, key issue to efficiently populate the databases of 3D models deployed in object/category recognition scenarios. As 3D Registration plays a fundamental role in 3D Reconstruction, the former part of the thesis proposes a pipeline for coarse registration of point clouds that is entirely based on the computation of 3D Local Reference Frames (LRF). 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, RGB-D and stereo cameras) as well as on quantitative comparison with respect to three other methods. Driven by the ever-lower costs and growing distribution of 3D sensing devices, we expect broad-scale integration of depth sensing into mobile devices to be forthcoming. Accordingly, the thesis investigates on merging appearance and shape information for Mobile Visual Search and focuses on encoding RGB-D images in compact binary codes. An extensive experimental analysis on three state-of-the-art datasets, addressing both category and instance recognition scenarios, has led to the development of an RGB-D search engine architecture that can attain high recognition rates with peculiarly moderate bandwidth requirements. Our experiments also include a comparison with the CDVS (Compact Descriptors for Visual Search) pipeline, candidate to become part of the MPEG-7 standard

Supervised Hashing with End-to-End Binary Deep Neural Network

Image hashing is a popular technique applied to large scale content-based visual retrieval due to its compact and efficient binary codes. Our work proposes a new end-to-end deep network architecture for supervised hashing which directly learns binary codes from input images and maintains good properties over binary codes such as similarity preservation, independence, and balancing. Furthermore, we also propose a new learning scheme that can cope with the binary constrained loss function. The proposed algorithm not only is scalable for learning over large-scale datasets but also outperforms state-of-the-art supervised hashing methods, which are illustrated throughout extensive experiments from various image retrieval benchmarks.Comment: Accepted to IEEE ICIP 201

Reflectance Hashing for Material Recognition

We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing materials due to its high-dimensionality. In this work, one-shot reflectance is captured using a unique optical camera measuring {\it reflectance disks} where the pixel coordinates correspond to surface viewing angles. The reflectance has class-specific stucture and angular gradients computed in this reflectance space reveal the material class. These reflectance disks encode discriminative information for efficient and accurate material recognition. We introduce a framework called reflectance hashing that models the reflectance disks with dictionary learning and binary hashing. We demonstrate the effectiveness of reflectance hashing for material recognition with a number of real-world materials