where λd and λt are constant hyperparameters balancing
between terms.
Implementation Details. We implement the Text2NeRF with
the Pytorch framework [42] and adopt TensoRF [39] as the
radiance ﬁeld. Note that, to make TensoRF satisfy the scene
generation in a large view range, we let the camera position
near the center of the NeRF bounding box and set outward-
facing viewpoints. For scene generation, we use the stable
diffusion [9] to generate the scene content related to the
input prompt and use the boosting monocular depth estimation
model [43] to estimate the depth for each view. In term of
depth alignment, the super-parameter M in Eq. 6 is set as
min(M0, 10000) in practice, where M0 indicates the number
of all matched points in the overlapping regions. Besides,
the depth alignment network in our framework uses the same
pixel-to-pixel U-net architecture as the depth merging network
in [43]. To train this network, we ﬁrst predict 10000 depth
Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields