Prior work to infer 3D texture use either texture atlases, which require uv-map**s and
hence have discontinuities, or colored voxels, which are memory inefficient and limited in …

In recent years, deep learning (DL) has shown great potential in the field of dermatological
image analysis. However, existing datasets in this domain have significant limitations …

We present an automated approach to detect and longitudinally track skin lesions on 3D
total-body skin surface scans. The acquired 3D mesh of the subject is unwrapped to a 2D …

Background and objectives: Advanced artificial intelligence and machine learning have
great potential to redefine how skin lesions are detected, mapped, tracked and documented …

Longitudinal tracking of skin lesions-finding correspondence, changes in morphology, and
texture-is beneficial to the early detection of melanoma. However, it has not been well …

We propose 3DBooSTeR, a novel method to recover a textured 3D body mesh from a
textured partial 3D scan. With the advent of virtual and augmented reality, there is a demand …

Melanoma is the most deadly form of skin cancer. Tracking the evolution of nevi and
detecting new lesions across the body is essential for the early detection of melanoma …

Abstract The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is
the first edition of a challenge fostering and benchmarking methods for recovering complete …

This study presents an algorithm to automatically extract the size and body shape of a 3D
scanned model. The methods used in this research include factor analysis, linear regression …

Reconstructing 3D human body shapes from 3D partial textured scans remains a
fundamental task for many computer vision and graphics applications–eg, body animation …