Dynamic NeRFs¶
The nerfacc.PropNetEstimator can natually work with dynamic NeRFs. To make the
nerfacc.OccGridEstimator also work with dynamic NeRFs, we need to make some compromises.
In these examples, we use the nerfacc.OccGridEstimator to estimate the
maximum opacity at each area over all the timestamps. This allows us to share the same estimator
across all the timestamps, including those timestamps that are not in the training set.
In other words, we use it to cache the union of the occupancy at all timestamps.
It is not optimal but still makes the rendering very efficient if the motion is not crazyly significant.
Performance Overview¶
updated on 2023-04-04
Methods |
Dataset |
Training Time \(\downarrow\) |
PSNR \(\uparrow\) |
LPIPS \(\downarrow\) |
|---|---|---|---|---|
TiNeuVox [1] |
HyperNeRF |
56.3min |
24.19 |
0.425 |
+nerfacc (occgrid) |
33.0min |
24.19 |
0.434 |
|
+nerfacc (propnet) |
34.3min |
24.26 |
0.398 |
|
TiNeuVox [1] |
D-NeRF |
11.8min |
31.14 |
0.050 |
+nerfacc (occgrid) |
4.2min |
31.75 |
0.038 |
|
K-Planes [2] |
D-NeRF |
63.9min |
30.28 |
0.043 |
+nerfacc (occgrid) |
38.8min |
30.35 |
0.042 |
|
T-NeRF [3] |
D-NeRF |
20hours |
28.78 |
0.069 |
+nerfacc (occgrid) |
58min |
32.22 |
0.040 |