  3D Gaussian Splatting is a novel method for 3D view synthesis, which can gain
an implicit neural learning rendering result than the traditional neural
rendering technology but keep the more high-definition fast rendering speed.
But it is still difficult to achieve a fast enough efficiency on 3D Gaussian
Splatting for the practical applications. To Address this issue, we propose the
I3DS, a synthetic model performance improvement evaluation solution and
experiments test. From multiple and important levels or dimensions of the
original 3D Gaussian Splatting, we made more than two thousand various kinds of
experiments to test how the selected different items and components can make an
impact on the training efficiency of the 3D Gaussian Splatting model. In this
paper, we will share abundant and meaningful experiences and methods about how
to improve the training, performance and the impacts caused by different items
of the model. A special but normal Integer compression in base 95 and a
floating-point compression in base 94 with ASCII encoding and decoding
mechanism is presented. Many real and effective experiments and test results or
phenomena will be recorded. After a series of reasonable fine-tuning, I3DS can
gain excellent performance improvements than the previous one. The project code
is available as open source.
