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Instant-NGP

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

Benchmarks

updated on 2022-10-12

Here we trained a Instant-NGP Nerf model on the Nerf-Synthetic dataset. We follow the same settings with the Instant-NGP paper, which uses train split for training and test split for evaluation. All experiments are conducted on a single NVIDIA TITAN RTX GPU. The training memory footprint is about 3GB.

Note

The Instant-NGP paper makes use of the alpha channel in the images to apply random background augmentation during training. For fair comparision, we rerun their code with a constant white background during both training and testing. Also it is worth to mention that we didn’t strictly follow the training receipe in the Instant-NGP paper, such as the learning rate schedule etc, as the purpose of this benchmark is to showcase instead of reproducing the paper.

PSNR

Lego

Mic

Materials

Chair

Hotdog

Ficus

Drums

Ship

MEAN

Instant-NGP 35k steps

35.87

36.22

29.08

35.10

37.48

30.61

23.85

30.62

32.35

(training time)

309s

258s

256s

316s

292s

207s

218s

250s

263s

Ours 20k steps

35.50

36.16

29.14

35.23

37.15

31.71

24.88

29.91

32.46

(training time)

287s

274s

269s

317s

269s

244s

249s

257s

271s