  We present a new system (NPBG++) for the novel view synthesis (NVS) task that
achieves high rendering realism with low scene fitting time. Our method
efficiently leverages the multiview observations and the point cloud of a
static scene to predict a neural descriptor for each point, improving upon the
pipeline of Neural Point-Based Graphics in several important ways. By
predicting the descriptors with a single pass through the source images, we
lift the requirement of per-scene optimization while also making the neural
descriptors view-dependent and more suitable for scenes with strong
non-Lambertian effects. In our comparisons, the proposed system outperforms
previous NVS approaches in terms of fitting and rendering runtimes while
producing images of similar quality.
