Light field camera with a microlens array can realize spatio-angular joint sampling of light ray field at the cost of a trade-off between spatial and angular resolutions. Alternatively, focal stack-based light field reconstruction can computationally retrieve full-pixel-resolution light fields in object space by virtue of the transport-of-intensity property in an image sequence recorded at different focal depths in image space. However, traditional camera imaging generally involves a nonlinear mapping between object and image spaces. The inconsistency of image-space recording and object-space reconstruction will reduce the accuracy of reconstructed light fields. In this work, we focus on analyzing and addressing the problem caused by the object-image space inconsistency for high-resolution, high-accuracy focal stack-based light field reconstruction. With a pre-calibrated light field camera as a reference, light field reconstructions in object and image spaces are experimentally compared and discussed from different aspects, such as digital refocusing, viewpoint switching, angular resolution, and depth range and sampling rate. All experimental results demonstrate that the light field reconstruction accuracy can be significantly improved when satisfying the object-image space consistency, which can serve as a mechanism for the realization and application of high-quality computational light field imaging and measurement in the situation of nonlinear recording and reconstruction.