Dynamic Scene

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

Benchmarks

updated on 2022-10-08

Here we trained a 8-layer-MLP for the radiance field and a 4-layer-MLP for the warping field, (similar to the T-Nerf model in the D-Nerf paper) on the D-Nerf dataset. We used train split for training and test split for evaluation. Our experiments are conducted on a single NVIDIA TITAN RTX GPU. The training memory footprint is about 11GB.

Note

The Occupancy Grid used in this example is shared by all the frames. In other words, instead of using it to indicate the opacity of an area at a single timestamp, Here we use it to indicate the maximum opacity at this area over all the timestamps. It is not optimal but still makes the rendering very efficient.

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
Ours (Training time)	37min	52min	69min	64min	44min	79min	79min	39min	58min