1. |
- Saint, A., et al.
(author)
-
3DBodyTex : Textured 3D body dataset
- 2018
-
In: Proceedings - 2018 International Conference on 3D Vision, 3DV 2018. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 495-504
-
Conference paper (peer-reviewed)abstract
- In this paper, a dataset, named 3DBodyTex, of static 3D body scans with high-quality texture information is presented along with a fully automatic method for body model fitting to a 3D scan. 3D shape modelling is a fundamental area of computer vision that has a wide range of applications in the industry. It is becoming even more important as 3D sensing technologies are entering consumer devices such as smartphones. As the main output of these sensors is the 3D shape, many methods rely on this information alone. The 3D shape information is, however, very high dimensional and leads to models that must handle many degrees of freedom from limited information. Coupling texture and 3D shape alleviates this burden, as the texture of 3D objects is complementary to their shape. Unfortunately, high-quality texture content is lacking from commonly available datasets, and in particular in datasets of 3D body scans. The proposed 3DBodyTex dataset aims to fill this gap with hundreds of high-quality 3D body scans with high-resolution texture. Moreover, a novel fully automatic pipeline to fit a body model to a 3D scan is proposed. It includes a robust 3D landmark estimator that takes advantage of the high-resolution texture of 3DBodyTex. The pipeline is applied to the scans, and the results are reported and discussed, showcasing the diversity of the features in the dataset.
|
|
2. |
|
|
3. |
|
|
4. |
|
|
5. |
|
|
6. |
- Gautam, S., et al.
(author)
-
Multigroup Multicast Precoding for Energy Optimization in SWIPT Systems With Heterogeneous Users
- 2020
-
In: IEEE Open Journal of the Communications Society. - : Institute of Electrical and Electronics Engineers (IEEE). - 2644-125X. ; 1, s. 92-108
-
Journal article (peer-reviewed)abstract
- The key to developing future generations of wireless communication systems lies in the expansion of extant methodologies, which ensures the coexistence of a variety of devices within a system. In this paper, we assume several multicasting (MC) groups comprising three types of heterogeneous users including Information Decoding (ID), Energy Harvesting (EH) and both ID and EH. We present a novel framework to investigate the multi-group (MG) - MC precoder designs for three different scenarios, namely, Separate Multicast and Energy Precoding Design (SMEP), Joint Multicast and Energy Precoding Design (JMEP), and Per-User Information and/or Energy Precoding Design (PIEP). In the considered system, a multi-antenna source transmits the relevant information and/or energy to the groups of corresponding receivers using more than one MC streams. The data processing users employ the conventional ID receiver architectures, the EH users make use of a non-linear EH module for energy acquisition, while the users capable of performing both ID and EH utilize the separated architecture with disparate ID and non-linear EH units. Our contribution is threefold. Firstly, we propose an optimization framework to i) minimize the total transmit power and ii) to maximize the sum harvested energy, the two key performance metrics of MG-MC systems. The proposed framework allows the analysis of the system under arbitrary given quality of service and harvested energy requirements. Secondly, to deal with the non-convexity of the formulated problems, we transform the original problems respectively into equivalent forms, which can be effectively solved by semi-definite relaxation (SDR) and alternating optimization. The convergence of the proposed algorithms is analytically guaranteed. Thirdly, a comparative study between the proposed schemes is conducted via extensive numerical results, wherein the benefits of adopting SMEP over JMEP and PIEP models are discussed.
|
|
7. |
|
|
8. |
|
|
9. |
|
|
10. |
|
|