Optimized loss function in deep learning profilometry for improved prediction performance
- Resource Type
- Authors
- Pieter G.G. Muyshondt; Sam Van der Jeught; Ivan Lobato
- Source
- JPhys Photonics
- Subject
- Computer science
business.industry
Deep learning
Physics
Pattern recognition
Profilometer
Function (mathematics)
Artificial intelligence
Electrical and Electronic Engineering
business
Atomic and Molecular Physics, and Optics
Electronic, Optical and Magnetic Materials
- Language
- English
- ISSN
- 2515-7647
Single-shot structured light profilometry (SLP) aims at reconstructing the 3D height map of an object from a single deformed fringe pattern and has long been the ultimate goal in fringe projection profilometry. Recently, deep learning was introduced into SLP setups to replace the task-specific algorithm of fringe demodulation with a dedicated neural network. Research on deep learning-based profilometry has made considerable progress in a short amount of time due to the rapid development of general neural network strategies and to the transferrable nature of deep learning techniques to a wide array of application fields. The selection of the employed loss function has received very little to no attention in the recently reported deep learning-based SLP setups. In this paper, we demonstrate the significant impact of loss function selection on height map prediction accuracy, we evaluate the performance of a range of commonly used loss functions and we propose a new mixed gradient loss function that yields a higher 3D surface reconstruction accuracy than any previously used loss functions.