3D Human Surface Reconstruction from a Single RGBD Scanner (Record no. 16577)

MARC details
000 -LEADER
fixed length control field 02749nam a22001697a 4500
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.6
Item number M432H
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Mathur, Preeti Balraj
245 ## - TITLE STATEMENT
Title 3D Human Surface Reconstruction from a Single RGBD Scanner
Statement of responsibility, etc by Preeti Balraj Mathur
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication IIT Jodhpur
Name of publisher Department of Computer Science and Technology
Year of publication 2023
300 ## - PHYSICAL DESCRIPTION
Number of Pages vii,16p.
Other physical details HB
500 ## - GENERAL NOTE
General note 3D reconstruction is the process of capturing the shape and appearance of real objects. Parametric 3D models have enabled a wide variety of tasks in computer graphics and vision, such as modeling human bodies, faces, and hands. However, the construction of these parametric models is often tedious, as it requires heavy manual tweaking, and they struggle to represent additional complexity and details such as wrinkles or clothing.<br/><br/>We propose to use an algebraic implicit surface method to model the 3D surface of the human body. It is evident that 3D shape representation has substantial effects on 3D shape reconstruction. Primitive-based representations approximate a 3D shape mainly by a set of simple implicit primitives, but the low geometrical complexity of the primitives limits the shape resolution. Moreover, setting a sufficient number of primitives for an arbitrary shape is challenging.<br/><br/>To overcome these issues, we propose a constrained implicit algebraic surface as the primitive with few learnable coefficients and higher geometrical complexities, and a deep neural network to produce these primitives. Our experiments demonstrate the superiority of our method in terms of representation power compared to the state-of-the-art methods in single RGB image 3D shape reconstruction. Furthermore, we show that our method can semantically learn segments of 3D shapes in an unsupervised manner.<br/><br/>The proposed approach is expected to learn some alternative methods, irrespective of the traditional parametric 3D models. Hence, it is expected that the proposed approach shall be in requisite of some handcrafted constraints to cope up with object specifications, primarily concentrating on disentangled 4D dynamics into latent space for the representation of human body shape and posture. This will be an enabler to leverage the flexibility developed recently into the learned implicit functions, and further fit the newly learned observations optimally into the parametric models for recognition of human shape and posture. Bearing to this, a significant impact is expected in terms of better accuracy and more detailing in the representation of the observed deformed sequences.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Department of Computer Science and Technology
Topical Term 3D Models
Topical Term MTech Theses
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Nagar, Rajendra
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Permanent Location Current Location Shelving location Date acquired Source of acquisition Full call number Accession Number Price effective from Koha item type
        Theses S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Reference 2024-04-01 Office of Academics 006.6 M432H TM00512 2024-06-28 Thesis