High-Density Monocular 3D Particle Image Velocimetry by Wavefront Shaping and Deep Learning

Abstract

Three-dimensional (3D) Particle Image Velocimetry (PIV) measures flow velocities by imaging laser-illuminated tracer particles seeded in a fluid, and is widely used in both academia and industry. Many applications require a compact setup for optical accessibility, ideally with a single camera, while also demanding high seeding densities for accurate velocimetry. These requirements, however, are typically incompatible: monocular methods break down at high densities; high-density measurements instead generally rely on multi-camera tomographic systems. Here, we introduce Point-spread-function-Engineering Training-based PIV (PET-PIV), a compact monocular 3D velocimetry approach that resolves this long-standing compactness-density trade-off through a minimal optical modification and deep-learning-based algorithms. PET-PIV requires only the insertion of a single thin phase mask to an otherwise conventional PIV setup, beneath the objective in microscopy or at the lens iris in macro-scale imaging, and calibrates the resulting imaging system in situ, making the approach straightforward to implement and readily scalable. Computationally, PET-PIV operates in two complementary regimes: a tracking/Lagrangian mode that localizes and links individual particles, and, more importantly, a field mode for ultra-high densities which directly reconstructs 3D velocity fields from 2D image sequences. In realistic experimental validations using a tomographic PIV system, PET-PIV demonstrates strong agreement with ground-truth references, with correlation coefficients (CC) exceeding 0.97, alongside an order-of-magnitude improvement in computational speed. Notably, PET-PIV enables monocular 3D macro-scale PIV at densities previously achievable only with multi-camera setups, dramatically expanding the applicability of 3D velocimetry in space-constrained and optically restricted environments.