The fundamental research of cavitation has been a hot topic for decades, while a particle image velocimetry (PIV) measurement on single bubble dynamics is uncommon due to many technical hassles. The involved flow field is small in space and highly unsteady in time, so it challenging the spatial and temporal resolution that current PIV techniques is able to achieve. In present study, single cavitation bubbles of hundreds of micrometers are seeded at near a solid wall by laser techniques. At the mean time a high-speed photography experiment up to 200,000 Hz, a planar PIV experiment combined with shadow method, and the first tomographic PIV experiment in the field, are meticulously designed and implemented. The images taken are firstly calibrated by a high-order anti-distortion algorithm before signal-to-noise ratio enhancement processing. According to the bubble morphology data, two simple formulas are fitted to describe the bubble collapse time and the bubble radius as functions of the wall-stand-off distance. A planar PIV shows the flow fields during the bubble oscillation, which helps to explain the mechanism of the formation of the two types of vortex left by a cavitation bubble. The consistence between the results of planar PIV and the 2D slices of tomographic data as well as the axial symmetry proved by the 3D velocity field data validates the sufficiency of a planar PIV measurement for flow fields induced by a single cavitation bubble. A principle named Proof-of-concept indeed guides the tomographic PIV experiment where the technical difficulties encountered and the resulting data may serve as a valuable starting point for future tomographic PIV experiments in the field of bubble dynamics. A SPCC PIV evaluation algorithm and an AI PIV approach, which are able to achieve a single-pixel-resolution velocity field, are implemented to resolve the wall shear rates exerted by a cavitation bubble. In the case when a bubble does not touch the wall the SPCC algorithm succeeds, while it is effort-consuming and badly influenced by unsteadiness among experiments. In particular, the flow fields are dominated by the many sources of instabilities for the moment long after the bubble generation, in which case the SPCC algorithm cannot deal with the images ensemble from different flow conditions. Although the AI deep learning method in present work is failed in obtaining the correct velocity gradient at the near-wall region, the there is a very bright way out. The governing law NS equations, the physical constrains non-slip conditon, and high accuracy PIV cross-correlation velocity data can be together considered within the loss function of a convolutional neural network. In such way the AI method is supposed to be capable to get the correct cavitation flow field up to single-pixel-resolution.