Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

PlaceAvoider: Steering First-Person Cameras away from Sensitive Spaces

Abstract—Cameras are now commonplace in our social and computing landscapes and embedded into consumer devices like smartphones and tablets. A new generation of wearable devices (such as Google Glass) will soon make ‘first-person ’ cameras nearly ubiquitous, capturing vast amounts of imagery without deliberate human action. ‘Lifelogging ’ devices and applications will record and share images from people’s daily lives with their social networks. These devices that automatically capture images in the background raise serious privacy concerns, since they are likely to capture deeply private information. Users of these devices need ways to identify and prevent the sharing of sensitive images. As a first step, we introduce PlaceAvoider, a technique for owners of first-person cameras to ‘blacklist ’ sensitive spaces (like bathrooms and bedrooms). PlaceAvoider recognizes images captured in these spaces and flags them for review before the images are made available to applications. PlaceAvoider performs novel image analysis using both fine-grained image features (like specific objects) and coarse-grained, scene-level features (like colors and textures) to classify where a photo was taken. PlaceAvoider combines these features in a probabilistic framework that jointly labels streams of images in order to improve accuracy. We test the technique on five realistic first-person image datasets and show it is robust to blurriness, motion, and occlusion. I

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Influence of a hybrid digital toolset on the creative behaviors of designers in early-stage design

The purpose of this research was to investigate how diversification of the repertoire of digital design techniques affects the creative behaviors of designers in the early design phases. The principal results of practice-based pilot experiments on the subject indicate three key properties of the hybrid digital tooling strategy. The strategy features intelligent human-machine integration, facilitating three different types of synergies between the designer and the digital media: human-dominated, machine-dominated, and a balanced human-machine collaboration. This strategy also boosts the cognitive behaviors of the designer by triggering divergent, transformative and convergent design activities and allowing for work on various abstraction levels. In addition, the strategy stimulates the explorative behaviors of the designer by encouraging the production of and interaction with a wide range of design representations, including physical and digital, dynamic and static objects. Thus, working with a broader range of digital modeling techniques can positively influence the creativity of designers in the early conception stages

Using 3D Visual Data to Build a Semantic Map for Autonomous Localization

Environment maps are essential for robots and intelligent gadgets to autonomously carry out tasks. Traditional maps built by visual sensors include metric ones and topological ones. These maps are navigation-oriented and not adequate for service robots or intelligent gadgets to interact with or serve human users who normally rely on conceptual knowledge or semantic contents of the environment. Therefore, semantic maps become necessary for building an effective human-robot interface. Although researchers from both robotics and computer vision domains have designed some promising systems, mapping with high accuracy and how to use semantic information for localization remain challenging. This thesis describes several novel methodologies to address these problems. RGB-D visual data is used as system input. Deep learning techniques and SLAM algorithms are combined in order to achieve better system performance. Firstly, a traditional feature based semantic mapping approach is presented. A novel matching error rejection algorithm is proposed to increase both loop closure detection and semantic information extraction accuracy. Evaluational experiments on public benchmark dataset are carried out to analyze the system performance. Secondly, a visual odometry system based on a Recurrent Convolutional Neural Network is presented for more accurate and robust camera motion estimation. The proposed network deploys an unsupervised end-to-end framework. The output transformation matrices are on an absolute scale, i.e. true scale in the real world. No data labeling or matrix post-processing tasks are required. Experiments show the proposed system outperforms other state-of-the-art VO systems. Finally, a novel topological localization approach based on the pre-built semantic maps is presented. Two streams of Convolutional Neural Networks are applied to infer locations. The additional semantic information in the maps is inversely used to further verify localization results. Experiments show the system is robust to viewpoint, lighting condition and object changes