  The success of the Neural Radiance Fields (NeRF) in novel view synthesis has
inspired researchers to propose neural implicit scene reconstruction. However,
most existing neural implicit reconstruction methods optimize per-scene
parameters and therefore lack generalizability to new scenes. We introduce
VolRecon, a novel generalizable implicit reconstruction method with Signed Ray
Distance Function (SRDF). To reconstruct the scene with fine details and little
noise, VolRecon combines projection features aggregated from multi-view
features, and volume features interpolated from a coarse global feature volume.
Using a ray transformer, we compute SRDF values of sampled points on a ray and
then render color and depth. On DTU dataset, VolRecon outperforms SparseNeuS by
about 30% in sparse view reconstruction and achieves comparable accuracy as
MVSNet in full view reconstruction. Furthermore, our approach exhibits good
generalization performance on the large-scale ETH3D benchmark.
