Fast and Explicit Neural View Synthesis

Understand

We study the problem of novel view synthesis from sparse source observations of a scene comprised of 3D objects.

• We propose a simple yet effective approach that is neither continuous nor implicit, challenging recent trends on view synthesis.
• Our approach explicitly encodes observations into a volumetric representation that enables amortized rendering.
• We demonstrate that although continuous radiance field representations have gained a lot of attention due to their expressive power, our simple approach obtains comparable or even better novel view reconstruction quality comparing with state-of-the-art baselines while increasing rendering speed by over 400x.