  Neural Radiance Fields (NeRFs), despite their outstanding performance on
novel view synthesis, often need dense input views. Many papers train one model
for each scene respectively and few of them explore incorporating multi-modal
data into this problem. In this paper, we focus on a rarely discussed but
important setting: can we train one model that can represent multiple scenes,
with 360$^\circ $ insufficient views and RGB-D images? We refer insufficient
views to few extremely sparse and almost non-overlapping views. To deal with
it, X-NeRF, a fully explicit approach which learns a general scene completion
process instead of a coordinate-based mapping, is proposed. Given a few
insufficient RGB-D input views, X-NeRF first transforms them to a sparse point
cloud tensor and then applies a 3D sparse generative Convolutional Neural
Network (CNN) to complete it to an explicit radiance field whose volumetric
rendering can be conducted fast without running networks during inference. To
avoid overfitting, besides common rendering loss, we apply perceptual loss as
well as view augmentation through random rotation on point clouds. The proposed
methodology significantly out-performs previous implicit methods in our
setting, indicating the great potential of proposed problem and approach. Codes
and data are available at https://github.com/HaoyiZhu/XNeRF.
