To apply Tomographic PIV to industrial flow, ex. flow around rain gutter, 50 mm volume thickness and 0.1 ppp particle density, at least, are necessary. In such experimental conditions, signal to ghost ratio of object images and accuracy of vectors are problems. To tackle these problems, influence of number of cameras, image pre-processing and vector post-processing to the accuracy of Tomographic PIV are experimentally investigated at different measurement volume thickness and particle densities. This investigation reveals that time-series minimum subtraction at each pixel without any spatial filter is suitable for image pre-processing for such conditions. Although this filter results in lower signal to ghost ratio, better vector field without less spurious vectors than a traditional one. This signal to ghost ratio is improved linearly by increasing the number of camera up to 8 and accuracy of velocity vectors also increasing up to 8 at any particle density and volume thickness conditions. This improvement by increasing the number of camera is experimentally proved as a first time. Obtained velocity vectors are filtered by spatial filter in physical domain and frequency domain to reduce measurement noise. Filtered velocity profile of thick volume measurable domain, 135 x 230 x 50 mm3 in air, is well coincident with previous experiments even in velocity fluctuation and Reynolds stress. To resolve turbulent fine scale vortex and large scale vortex simultaneously, more than 6-camera are needed for the case of 0.12 ppp particle density and 50 mm volume thickness. For the case of 0.45 ppp and 50 mm volume thickness with 8-camera has also enough accuracy to access velocity fluctuation and Reynolds stress. This paper achieves the large measurable domain, 160 x 220 x 80 mm3, at the particle density of 0.53 ppp. 自適應(yīng)粒子成像測速場儀（PIV） 德國LaVision PIV/PLIF粒子成像測速場儀 體視層析粒子成像測速系統(tǒng)(Tomo-PIV)