  We present Neural Pixel Composition (NPC), a novel approach for continuous
3D-4D view synthesis given only a discrete set of multi-view observations as
input. Existing state-of-the-art approaches require dense multi-view
supervision and an extensive computational budget. The proposed formulation
reliably operates on sparse and wide-baseline multi-view imagery and can be
trained efficiently within a few seconds to 10 minutes for hi-res (12MP)
content, i.e., 200-400X faster convergence than existing methods. Crucial to
our approach are two core novelties: 1) a representation of a pixel that
contains color and depth information accumulated from multi-views for a
particular location and time along a line of sight, and 2) a multi-layer
perceptron (MLP) that enables the composition of this rich information provided
for a pixel location to obtain the final color output. We experiment with a
large variety of multi-view sequences, compare to existing approaches, and
achieve better results in diverse and challenging settings. Finally, our
approach enables dense 3D reconstruction from sparse multi-views, where COLMAP,
a state-of-the-art 3D reconstruction approach, struggles.
