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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