  We propose a differentiable rendering algorithm for efficient novel view
synthesis. By departing from volume-based representations in favor of a learned
point representation, we improve on existing methods more than an order of
magnitude in memory and runtime, both in training and inference. The method
begins with a uniformly-sampled random point cloud and learns per-point
position and view-dependent appearance, using a differentiable splat-based
renderer to evolve the model to match a set of input images. Our method is up
to 300x faster than NeRF in both training and inference, with only a marginal
sacrifice in quality, while using less than 10~MB of memory for a static scene.
For dynamic scenes, our method trains two orders of magnitude faster than
STNeRF and renders at near interactive rate, while maintaining high image
quality and temporal coherence even without imposing any temporal-coherency
regularizers.
