324 research outputs found
Integration of Multisensorial Stimuli and Multimodal Interaction in a Hybrid 3DTV System
This article proposes the integration of multisensorial stimuli and multimodal interaction components into a sports multimedia asset under two dimensions: immersion and interaction. The first dimension comprises a binaural audio system and a set of sensory effects synchronized with the audiovisual content, whereas the second explores interaction through the insertion of interactive 3D objects into the main screen and on-demand presentation of additional information in a second touchscreen. We present an end-to-end solution integrating these components into a hybrid (internet-broadcast) television system using current 3DTV standards. Results from an experimental study analyzing the perceived quality of these stimuli and their influence on the Quality of Experience are presented
SurfelWarp: Efficient Non-Volumetric Single View Dynamic Reconstruction
We contribute a dense SLAM system that takes a live stream of depth images as
input and reconstructs non-rigid deforming scenes in real time, without
templates or prior models. In contrast to existing approaches, we do not
maintain any volumetric data structures, such as truncated signed distance
function (TSDF) fields or deformation fields, which are performance and memory
intensive. Our system works with a flat point (surfel) based representation of
geometry, which can be directly acquired from commodity depth sensors. Standard
graphics pipelines and general purpose GPU (GPGPU) computing are leveraged for
all central operations: i.e., nearest neighbor maintenance, non-rigid
deformation field estimation and fusion of depth measurements. Our pipeline
inherently avoids expensive volumetric operations such as marching cubes,
volumetric fusion and dense deformation field update, leading to significantly
improved performance. Furthermore, the explicit and flexible surfel based
geometry representation enables efficient tackling of topology changes and
tracking failures, which makes our reconstructions consistent with updated
depth observations. Our system allows robots to maintain a scene description
with non-rigidly deformed objects that potentially enables interactions with
dynamic working environments.Comment: RSS 2018. The video and source code are available on
https://sites.google.com/view/surfelwarp/hom
A Brief Survey of Image-Based Depth Upsampling
Recently, there has been remarkable growth of interest in the development and applications of Time-of-Flight (ToF) depth cameras. However, despite the permanent improvement of their characteristics, the practical applicability of ToF cameras is still limited by low resolution and quality of depth measurements. This has motivated many researchers to combine ToF cameras with other sensors in order to enhance and upsample depth images. In this paper, we compare ToF cameras to three image-based techniques for depth recovery, discuss the upsampling problem and survey the approaches that couple ToF depth images with high-resolution optical images. Other classes of upsampling methods are also mentioned
Image-guided ToF depth upsampling: a survey
Recently, there has been remarkable growth of interest in the development and applications of time-of-flight (ToF) depth cameras. Despite the permanent improvement of their characteristics, the practical applicability of ToF cameras is still limited by low resolution and quality of depth measurements. This has motivated many researchers to combine ToF cameras with other sensors in order to enhance and upsample depth images. In this paper, we review the approaches that couple ToF depth images with high-resolution optical images. Other classes of upsampling methods are also briefly discussed. Finally, we provide an overview of performance evaluation tests presented in the related studies
Real-time video-plus-depth content creation utilizing time-of-flight sensor - from capture to display
Recent developments in 3D camera technologies, display technologies and other related fields have been aiming to provide 3D experience for home user and establish services such as Three-Dimensional Television (3DTV) and Free-Viewpoint Television (FTV). Emerging multiview autostereoscopic displays do not require any eyewear and can be watched by multiple users at the same time, thus are very attractive for home environment usage. To provide a natural 3D impression, autostereoscopic 3D displays have been design to synthesize multi-perspective virtual views of a scene using Depth-Image-Based Rendering (DIBR) techniques. One key issue of DIBR is that scene depth information in a form of a depth map is required in order to synthesize virtual views. Acquiring this information is quite complex and challenging task and still an active research topic.
In this thesis, the problem of dynamic 3D video content creation of real-world visual scenes is addressed. The work assumed data acquisition setting including Time-of-Flight (ToF) depth sensor and a single conventional video camera. The main objective of the work is to develop efficient algorithms for the stages of synchronous data acquisition, color and ToF data fusion, and final view-plus-depth frame formatting and rendering.
The outcome of this thesis is a prototype 3DTV system capable for rendering live 3D video on a 3D autostereoscopic display. The presented system makes extensive use of the processing capabilities of modern Graphics Processing Units (GPUs) in order to achieve real-time processing rates while providing an acceptable visual quality. Furthermore, the issue of arbitrary view synthesis is investigated in the context of DIBR and a novel approach based on depth layering is proposed. The proposed approach is applicable for general virtual views synthesis, i.e. in terms of different camera parameters such as position, orientation, focal length and varying sensors spatial resolutions. The experimental results demonstrate real-time capability of the proposed method even for CPU-based implementations. It compares favorably to other view synthesis methods in terms of visual quality, while being more computationally efficient
InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure
Volumetric models have become a popular representation for 3D scenes in
recent years. One breakthrough leading to their popularity was KinectFusion,
which focuses on 3D reconstruction using RGB-D sensors. However, monocular SLAM
has since also been tackled with very similar approaches. Representing the
reconstruction volumetrically as a TSDF leads to most of the simplicity and
efficiency that can be achieved with GPU implementations of these systems.
However, this representation is memory-intensive and limits applicability to
small-scale reconstructions. Several avenues have been explored to overcome
this. With the aim of summarizing them and providing for a fast, flexible 3D
reconstruction pipeline, we propose a new, unifying framework called InfiniTAM.
The idea is that steps like camera tracking, scene representation and
integration of new data can easily be replaced and adapted to the user's needs.
This report describes the technical implementation details of InfiniTAM v3,
the third version of our InfiniTAM system. We have added various new features,
as well as making numerous enhancements to the low-level code that
significantly improve our camera tracking performance. The new features that we
expect to be of most interest are (i) a robust camera tracking module; (ii) an
implementation of Glocker et al.'s keyframe-based random ferns camera
relocaliser; (iii) a novel approach to globally-consistent TSDF-based
reconstruction, based on dividing the scene into rigid submaps and optimising
the relative poses between them; and (iv) an implementation of Keller et al.'s
surfel-based reconstruction approach.Comment: This article largely supersedes arxiv:1410.0925 (it describes version
3 of the InfiniTAM framework
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