The Lagrangian Particle Image Velocimetry (LAPIV) method was firstly proposed in Yang et al. (2019) as a prototype approach to achieve the goal of accurate and efficient reconstruction of 3D Eulerian velocity field of fluid flow from multi-view particle images. After validating against synthetic datasets, the prototype has already shown significant advantages in revealing more small scale flow structures than other state-of-the-art Eulerian velocity estimation methods, such as TomoPIV (Scarano, 2013) and VIC# (Jeon et al., 2019). However, at this early stage, LAPIV can not be easily applied to other datasets. In the current work, we focus on extending LAPIV to operational search by incorporating several essential and well-established paradigms: multi-resolution, warping, and proxy regularization. The resulting system to solve for the regularized flow remains light-weight compared to other optimization methods anchoring either the image or the particle field as the data. We show that LAPIV can yield competitive, if not better, results compared to other state-of-the-art approaches facing versatile datasets.