ABSTRACT:The paper presents a collaborative image-based 3D reconstruction pipeline to perform image acquisition with a smartphone and geometric 3D reconstruction on a server during concurrent or disjoint acquisition sessions. Images are selected from the video feed of the smartphone's camera based on their quality and novelty. The smartphone's app provides on-the-fly reconstruction feedback to users co-involved in the acquisitions. The server is composed of an incremental SfM algorithm that processes the received images by seamlessly merging them into a single sparse point cloud using bundle adjustment. Dense image matching algorithm can be lunched to derive denser point clouds. The reconstruction details, experiments and performance evaluation are presented and discussed.
During the last two decades we have witnessed great improvements in ICT hardware and software technologies. Three-dimensional content is starting to become commonplace now in many applications. Although for many years 3D technologies have been used in the generation of assets by researchers and experts, nowadays these tools are starting to become commercially available to every citizen. This is especially the case for smartphones, that are powerful enough and sufficiently widespread to perform a huge variety of activities (e.g. paying, calling, communication, photography, navigation, localization, etc.), including just very recently the possibility of running 3D reconstruction pipelines. The REPLICATE project is tackling this particular issue, and it has an ambitious vision to enable ubiquitous 3D creativity via the development of tools for mobile 3D-assets generation on smartphones/tablets. This article presents the REPLICATE project’s concept and some of the ongoing activities, with particular attention being paid to advances made in the first year of work. Thus the article focuses on the system architecture definition, selection of optimal frames for 3D cloud reconstruction, automated generation of sparse and dense point clouds, mesh modelling techniques and post-processing actions. Experiments so far were concentrated on indoor objects and some simple heritage artefacts, however, in the long term we will be targeting a larger variety of scenarios and communities.
This paper describes an automatically annotated multimodal corpus of multi-party meetings. The corpus provides for each subject involved in the experimental sessions information on her/his social behavior and personality traits, as well as audiovisual cues (speech rate, pitch and energy, head orientation, head, hand and body fidgeting). The corpus is based on the audio and video recordings of thirteen sessions, which took place in a lab setting equipped with cameras and microphones. Our main concern in collecting this corpus was to investigate the possibility of creating a system capable of automatically analyzing social behaviors and predicting personality traits using audio-visual cues.
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