  Neural radiance fields (NeRF) have shown great potentials in representing 3D
scenes and synthesizing novel views, but the computational overhead of NeRF at
the inference stage is still heavy. To alleviate the burden, we delve into the
coarse-to-fine, hierarchical sampling procedure of NeRF and point out that the
coarse stage can be replaced by a lightweight module which we name a neural
sample field. The proposed sample field maps rays into sample distributions,
which can be transformed into point coordinates and fed into radiance fields
for volume rendering. The overall framework is named as NeuSample. We perform
experiments on Realistic Synthetic 360$^{\circ}$ and Real Forward-Facing, two
popular 3D scene sets, and show that NeuSample achieves better rendering
quality than NeRF while enjoying a faster inference speed. NeuSample is further
compressed with a proposed sample field extraction method towards a better
trade-off between quality and speed.
