TimeNeRF: Building Generalizable Neural Radiance Fields across Time from Few-Shot Input Views

TL;DR

TimeNeRF is a novel neural rendering method that generalizes across time and views, enabling realistic scene synthesis at arbitrary times with minimal input views, without scene-specific retraining.

Contribution

It introduces a generalizable neural radiance field framework capable of modeling temporal scene changes from few-shot inputs, a significant advancement over existing NeRF techniques.

Findings

Effective in few-shot settings without per-scene optimization.

Capable of realistic view synthesis across different times of day.

Successfully models natural scene transitions from dawn to dusk.

Abstract

We present TimeNeRF, a generalizable neural rendering approach for rendering novel views at arbitrary viewpoints and at arbitrary times, even with few input views. For real-world applications, it is expensive to collect multiple views and inefficient to re-optimize for unseen scenes. Moreover, as the digital realm, particularly the metaverse, strives for increasingly immersive experiences, the ability to model 3D environments that naturally transition between day and night becomes paramount. While current techniques based on Neural Radiance Fields (NeRF) have shown remarkable proficiency in synthesizing novel views, the exploration of NeRF's potential for temporal 3D scene modeling remains limited, with no dedicated datasets available for this purpose. To this end, our approach harnesses the strengths of multi-view stereo, neural radiance fields, and disentanglement strategies across diverse datasets. This equips our model with the capability for generalizability in a few-shot setting, allows us to construct an implicit content radiance field for scene representation, and further enables the building of neural radiance fields at any arbitrary time. Finally, we synthesize novel views of that time via volume rendering. Experiments show that TimeNeRF can render novel views in a few-shot setting without per-scene optimization. Most notably, it excels in creating realistic novel views that transition smoothly across different times, adeptly capturing intricate natural scene changes from dawn to dusk.