E. Training and Implementation Details
Training Objective. We use a RGB loss, a depth loss, and
a transmittance loss to optimize the radiance ﬁeld of the
3D scene. Like previous NeRF-based works [13], [39], [40],
the RGB loss LRGB is deﬁned as a L2 loss between the
rendered pixel color CR and the color C generated by the
diffusion model fd. Different from previous works that employ
regularized depth losses to handle uncertainty or scale-variant
problem [17], [41], we adopt a stricter depth loss LDepth to
minimize the L2 distance between the rendered depth DR and
the aligned estimated depth ˆD, since the aligned depth maps
used in our framework are scale-invariant and can be regarded
as ground truth. Besides, inspired by [1], we design a depth-
aware transmittance loss LT to encourage the NeRF network
to produce empty density before the camera ray reaching the
expected depth ˆz:

LT = (cid:107)T (t) · m(t)(cid:107)2

(9)

6
Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields