Vanilla NeRF

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

Radiance Field

We follow the original NeRF paper to implement a 8-layer-MLP radiance field (examples/radiance_fields/mlp.py) with positional encoding.

Note

The vanilla Nerf paper uses two MLPs for course-to-fine sampling. Instead here we only use a single MLP with more samples (1024). Both ways share the same spirit to do dense sampling around the surface. Our fast rendering inheritly skip samples away from the surface so we can simplly increase the number of samples with a single MLP, to achieve the same goal with the coarse-to-fine sampling, without runtime or memory issue.

Benchmark: Nerf-Synthetic Dataset

updated on 2022-10-08

Our experiments are conducted on a single NVIDIA TITAN RTX GPU. The training memory footprint is about 10GB.

PSNR	Lego	Mic	Materials	Chair	Hotdog	Ficus	Drums	Ship	MEAN
NeRF (~ days)	32.54	32.91	29.62	33.00	36.18	30.13	25.01	28.65	31.00
Ours (~ 1 hr)	33.69	33.76	29.73	33.32	35.80	32.52	25.39	28.18	31.55