Results 41 to 50 of about 1,530,615 (334)
SurfNet: Generating 3D shape surfaces using deep residual networks
, 2017 3D shape models are naturally parameterized using vertices and faces, \ie, composed of polygons forming a surface. However, current 3D learning paradigms for predictive and generative tasks using convolutional neural networks focus on a voxelized ...
Huang, Qixing +3 more
core +1 more source
Learning quadrangulated patches for 3D shape parameterization and completion
, 2017 We propose a novel 3D shape parameterization by surface patches, that are oriented by 3D mesh quadrangulation of the shape. By encoding 3D surface detail on local patches, we learn a patch dictionary that identifies principal surface features of the ...
Sarkar, Kripasindhu +2 more
core +1 more source
Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation [PDF]
, 2020 Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait ...
Luo, Jian, Tjahjadi, Tardi
core +1 more source
fMRI Evidence for Multi-level Adaptation of Stereoscopic Curvature Aftereffect
Taiyuan Ligong Daxue xuebaoPurposes In this study, a functional resonance imaging adaptation (fMRIa) experiment was conducted to examine the multi-level adaptation mechanism in 16 ROIs of the human brain cortex.
YAN Pengfei +3 more
doaj +1 more source
Synthesizing average 3D anatomical shapes
NeuroImage, 2006 Average shape estimates are often used to characterize normal morphological variation and discriminate dysmorphology in populations. The purpose of this paper is to estimate "average" or the most representative shapes in populations by using high-resolution medical images as input.
Gary E, Christensen +2 more
openaire +2 more sources
Deep Learning Representation using Autoencoder for 3D Shape Retrieval
, 2014 We study the problem of how to build a deep learning representation for 3D shape. Deep learning has shown to be very effective in variety of visual applications, such as image classification and object detection.
Bai, Song +4 more
core +1 more source
PhotoShape: Photorealistic Materials for Large-Scale Shape Collections
, 2018 Existing online 3D shape repositories contain thousands of 3D models but lack photorealistic appearance. We present an approach to automatically assign high-quality, realistic appearance models to large scale 3D shape collections.
Farhadi, Ali +3 more
core +1 more source
Inconsistency of perceived 3D shape
Vision Research, 2010 Internal consistency of local depth, slant, and curvature judgments was studied by asking participants to match two 3D surfaces rendered by different mixtures of 3D cues (velocity, texture, and shading). We found that perceptual judgments were not consistent with each other, with cue-specific distortions.
M. Di Luca, F. Domini, CAUDEK, CORRADO
openaire +4 more sources
3D Shape Analysis of Scoliosis
, 2023 Scoliosis is typically measured in 2D in the coronal plane, although it is a three-dimensional (3D) condition. Our objective in this work is to analyse the 3D geometry of the spine and its relationship to the vertebral canal. To this end, we make three contributions: first, we extract the 3D space curve of the spine automatically from low-resolution ...
Bourigault, E +5 more
openaire +2 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT Bone tumours present significant challenges for affected patients, as multimodal therapy often leads to prolonged physical limitations. This is particularly critical during childhood and adolescence, as it can negatively impact physiological development and psychosocial resilience.
Jennifer Queisser +5 more
wiley +1 more source