Special Issue "Sensors Fusion for Human-Centric 3D Capturing"
Deadline for manuscript submissions: closed (31 December 2020).
Interests: UAV detection and classification; 3D/4D computer vision; 3D human reconstruction and motion capturing; medical image processing
Special Issues and Collections in MDPI journals
Interests: Visual Processing including: sign language recognition, object/person detection & tracking, deep learning, federated learning
This Special Issue tries to capture the recent advances in 3D capture technology by fusing data from multiple sensors (be it cameras, inertial, infrared or depth sensors) to produce high-quality 3D human representations (i.e., 3D motion, shape, appearance, performance, and activity).
Nowadays, given the ubiquity of consumer-grade capturing devices, in conjunction with the revolutionary advance of today’s powerful and efficient processing capacities (i.e., modern GPUs and even newer alternatives for efficient on-device hardware solutions), 3D human capturing can be characterized as one of the most beneficial and emerging technologies. The newest generation of commodity low-cost sensing devices are typically capable of capturing a multitude of different modalities (i.e., color, infrared, orientation, structure, as well as higher-level information like device pose or human motion).
These technologies will greatly influence a number of industrial sectors like gaming, creative industries (media, marketing, and the overall XR spectrum), film (VFX), and the enabling of new forms of human–computer interactions. Human perception and understanding as well as their accurate visual representations are important objectives for these industries.
With respect to the wider media sector, current immersive experiences allow for three degrees of freedom—3DOF (omnidirectional)—viewing, but the development of new presentations means (VR HMDs as well as new 3D TVs/displays) will steer their evolution towards 3DOF+ (allowing for limited translations) and 6DOF (unrestricted viewpoint selection) experiences.
From a systemic point of view, the integration of multiple modalities (either homogeneous or heterogeneous) is a challenging task that needs to address issues like spatial and/or temporal alignment, multi-modal sensor fusion (either direct or indirect using a priori knowledge), and real-time operational capacity. Additionally, operating in real-world conditions imposes extra challenges besides real-time performance like limited power usage, constrained deployment capabilities, and the necessary robustness. This is further accentuated by the need to commercialize this technology and thus utilize low-cost sensors in an efficient and effective manner. Though recent advances in sensor technology have managed to reduce sensor costs, they cannot overcome the inherent limitations of each modality. The need to develop robust systems still necessitates sensor fusion techniques. Therefore, the design of multi-modal and/or multi-sensor systems requires the maturing of existing, and the development of new, technologies.
As demonstrated by recent research, sensor information fusion can greatly increase the accuracy and performance of such systems as multi-modality usually offers complementarity. While depth sensing provides 3D information, its commercial counterparts do it at the cost of accuracy and resolution that modern miniaturized cameras have mostly addressed. On the other hand, it can better handle color variations (textures and lighting changes) compared to traditional color cameras. Similarly, while inertial units provide live orientation information, their global spatial alignment is an issue that can be guided by depth sensors while at the same time guaranteeing drift-free data collection. Furthermore, deep networks have started showcasing promising performance in terms of domain alignment and transfer learning, paving the way for their exploitation in the context of sensor fusion. Nonetheless, more research is needed to mature their applicability and allow for multi-sensor data training and/or incorporation of prior modality knowledge when operating in a multi-modal manner.
This Special Issue invites contributions that address multi-sensor and multi-modal information fusion with the aim of capturing humans in 3D. It aims at capturing the current and emerging statues in the human capturing relevant technologies like 3D reconstruction, motion, and actions. In particular, submitted papers should clearly show novel contributions and innovative applications covering but not limited to any of the following topics around 3D human capturing using multiple sensor modalities:
- Multi-modal data fusion;
- Multi-sensor alignment;
- Sensor data denoising and completion;
- Multi-modal learning for sensor domain invariant representations;
- Cross-modality transfer learning;
- Self-supervised multi-modal learning;
- Multi-sensor and multi-modal capturing systems;
- Multi-modal dynamic scene capturing;
- Open source frameworks and libraries for working with multi-modal sensors;
- Multi-modal and multi-sensor applications (HCI, 3D capture for XR and/or free-viewpoint video, tele-presence, motion capture, real-time action recognition, simultaneous body, hands and face capture, non-rigid 3D reconstruction of humans, real-time calibration systems, and systems integrating multiple sensor types).
Dr. Dimitrios Zarpalas
Dr. Petros Daras
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- sensor fusion
- multi-modal learning
- multi-sensor systems
- multi-view systems
- 3D reconstruction
- motion capture
- 3D vision
- volumetric capture
- 3D action recognition
- wearable sensors
- body sensor networks
- infrared vision
- depth sensing