  Most NeRF-based models are designed for learning the entire scene, and
complex scenes can lead to longer learning times and poorer rendering effects.
This paper utilizes scene semantic priors to make improvements in fast
training, allowing the network to focus on the specific targets and not be
affected by complex backgrounds. The training speed can be increased by 7.78
times with better rendering effect, and small to medium sized targets can be
rendered faster. In addition, this improvement applies to all NeRF-based
models. Considering the inherent multi-view consistency and smoothness of NeRF,
this paper also studies weak supervision by sparsely sampling negative ray
samples. With this method, training can be further accelerated and rendering
quality can be maintained. Finally, this paper extends pixel semantic and color
rendering formulas and proposes a new scene editing technique that can achieve
unique displays of the specific semantic targets or masking them in rendering.
To address the problem of unsupervised regions incorrect inferences in the
scene, we also designed a self-supervised loop that combines morphological
operations and clustering.
