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IMPROVING_LOG.md

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Improving log

Custom CUDA implementation for efficiency

Some intermediate steps are reimplemented in cuda (lib/cuda/), which improves training speed by 1.8~3.5. Below show the results dense grid under 256^3 voxels and 160^3 voxels. Telsa V100, RTX 2080 Ti, and RTX 1080 Ti are tested. The PSNRs of different versions on different machines have about 0.2 PSNR drift. The training speeds of the final version are improved 2--3 times from the original native pytorch implementation.

num_voxels=256^3 lego mic ship
GPU=V100 psnr mm:ss psnr mm:ss psnr mm:ss
native pytorch
b076912		 35.51 15:10 34.39 14:11 30.05 17:04
cuda re-impl. Adam optimizer
d3783f4		 35.47 08:54 (1.7x) 34.34 06:41 (2.1x) 30.05 10:23 (1.6x)
cuda re-impl. rendering
3de7a6d		 35.63 06:31 (2.3x) 34.48 04:31 (3.1x) 30.30 08:20 (2.0x)
prevent atomic add in alpha2weight
4f4ac99		 35.61 05:35 (2.7x) 34.51 04:00 (3.5x) 30.29 07:20 (2.3x)
GPU=2080Ti
native pytorch b076912 - OOM 34.44 18:01 - OOM
cuda re-impl. 4f4ac99 35.61 07:19 34.49 04:30 (4.0x) 30.29 09:53
GPU=1080Ti
native pytorch b076912 35.76 37:22 34.47 31:18 30.09 45:28
cuda re-impl. 4f4ac99 35.62 14:32 (2.6x) 34.50 08:55 (3.5x) 30.29 21:00 (2.2x) 
# The model&training config for the results above
coarse_train = dict(N_iters=5000)
fine_train = dict(pg_scale=[1000,2000,3000,4000,5000,6000])
fine_model_and_render = dict(num_voxels=256**3)
num_voxels=160^3 lego mic ship
GPU=V100 psnr mm:ss psnr mm:ss psnr mm:ss
native pytorch
b076912		 34.65 08:29 33.19 07:04 29.08 10:38
cuda re-impl. Adam optimizer
d3783f4		 34.66 06:01 (1.4x) 33.14 04:38 (1.5x) 29.04 08:06 (1.3x)
cuda re-impl. rendering
3de7a6d		 34.56 04:50 (1.8x) 33.10 03:22 (2.1x) 29.19 06:31 (1.6x)
prevent atomic add in alpha2weight
4f4ac99		 34.58 03:58 (2.1x) 33.12 03:00 (2.4x) 29.17 05:46 (1.8x)
GPU=2080Ti
native pytorch b076912 34.68 11:27 33.18 09:19 29.13 14:35
cuda re-impl. 4f4ac99 34.59 04:59 (2.3x) 33.15 03:04 (3.0x) 29.19 07:32 (1.9x)
GPU=1080Ti
native pytorch b076912 34.66 22:01 33.19 17:14 29.10 29:57
cuda re-impl. 4f4ac99 34.56 10:29 (2.1x) 33.11 06:21 (2.7x) 29.18 16:48 (x1.8) 
# The model&training config for the results above
coarse_train = dict(N_iters=5000)
fine_train = dict(pg_scale=[1000,2000,3000,4000])
fine_model_and_render = dict(num_voxels=160**3)

Extend for forward-facing scene

The model for forward-facing scene is implemented in lib/dmpigo.py. Some main modifications include:

  • Use NeRF's NDC warping
  • Use Multiplane Image
  • The initial probability stopping at each plane is 1/(# of planes)
  • Skip coarse stage training as it don't help in forward-facing scene
  • Adopt total variation loss or the quality would degrade

All config files are in configs/llff/. The based config for small model is:

# See configs/llff/llff_default.py
data = dict(
    dataset_type='llff',    # use llff dataloader
    ndc=True,               # use ndc coordinate (only for forward-facing; not support yet)
    width=1008,             # enforce image width
    height=756,             # enforce image height
)

coarse_train = dict(
    N_iters=0,              # we don't need the coarse stage training
)

fine_train = dict(
    N_iters=30000,
    N_rand=4096,            # it seem that larger batch don't help
    pg_scale=[2000,4000,6000,8000],
    ray_sampler='flatten',
    tv_before=1e9,          # enable total variation loss
    tv_dense_before=10000,  # dense version of total variation loss for the first 10k iterations
    weight_tv_density=1e-5,
    weight_tv_k0=1e-6,
)

fine_model_and_render = dict(
    num_voxels=256**3,
    mpi_depth=128,          # the number of planes in Multiplane Image (work when ndc=True)
    rgbnet_dim=9,           # it seem that more rgbnet_dim don't help
    rgbnet_width=64,        # it seem that larger rgbnet_width don't help
    world_bound_scale=1,    # we don't have to slightly enlarge the ndc
    fast_color_thres=1e-3,  # the initial probability stopping at each plane is 1/mpi_depth
                            # so the original 1e-4 would be too passive here
)

See configs/llff/llff_default_lg.py for the modification for large model. Basically, we double the number of mpi_depth and use a larger MLP.

Results:

  • Our training times are measured on single Telsa V100 GPU.
  • Training time (mm:ss)
    Method Avg. Room Fern Leaves Fortress Orchids Flower T-Rex Horns
    NeRF 30+ hr
    Ours small 05:30 05:55 06:12 04:36 05:38 05:26 05:28 05:07 05:23
    Ours large 16:27 17:38 18:21 14:11 16:03 17:14 16:27 15:46 16:00
  • PSNR
    Method Avg. Room Fern Leaves Fortress Orchids Flower T-Rex Horns
    NeRF 26.50 32.70 25.17 20.92 31.16 20.36 27.40 26.80 27.45
    Ours small 25.83 30.88 24.69 20.81 30.09 19.82 27.34 26.04 26.98
    Ours large 26.37 32.16 24.99 21.01 30.79 20.07 27.62 26.63 27.69
  • SSIM
    Method Avg. Room Fern Leaves Fortress Orchids Flower T-Rex Horns
    NeRF 0.811 0.948 0.792 0.690 0.881 0.641 0.827 0.880 0.828
    Ours small 0.826 0.940 0.810 0.735 0.871 0.663 0.849 0.891 0.850
    Ours large 0.840 0.951 0.821 0.745 0.890 0.673 0.856 0.909 0.877
  • LPIPS (VGG)
    Method Avg. Room Fern Leaves Fortress Orchids Flower T-Rex Horns
    NeRF 0.250 0.178 0.280 0.316 0.171 0.321 0.219 0.249 0.268
    Ours small 0.215 0.191 0.231 0.215 0.185 0.252 0.187 0.229 0.233
    Ours large 0.200 0.172 0.222 0.205 0.161 0.247 0.181 0.215 0.203