T-NeRF

See code examples/train_mlp_dnerf.py at our github repository for details.

Radiance Field

Here we implement a very basic time-conditioned NeRF (T-NeRF) model (examples/radiance_fields/mlp.py) for dynamic scene reconstruction. The implementation is mostly follow the T-NeRF described in the D-NeRF paper, with a 8-layer-MLP for the radiance field and a 4-layer-MLP for the warping field. The only major difference is that we reduce the max frequency of the positional encoding from 10 to 4, to respect the fact that the motion of the object is relatively smooth.

Benchmarks: D-NeRF Dataset

updated on 2022-10-08

Our experiments are conducted on a single NVIDIA TITAN RTX GPU. The training memory footprint is about 11GB.

PSNR	bouncing balls	hell warrior	hook	jumping jacks	lego	mutant	standup	trex	MEAN
D-NeRF (~ days)	32.80	25.02	29.25	32.80	21.64	31.29	32.79	31.75	29.67
Ours (~ 1 hr)	39.49	25.58	31.86	32.73	24.32	35.55	35.90	32.33	32.22