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