Closed 1ssb closed 1 year ago
The instructions for generating the ShapeNet datasets can be found in the README. The final cell of the Colab notebook demonstrates how to perform inference on a test image and viewpoint:
test_W_i = E(test_source_image)
(_, C_rs_f) = run_one_iter_of_pixelnerf(
test_ds,
N_c,
t_i_c_bin_edges,
t_i_c_gap,
test_os,
camera_distance,
scale,
test_W_i,
chunk_size,
F_c,
N_f,
t_f,
N_d,
d_std,
t_n,
F_f,
)
plt.imshow(C_rs_f.detach().cpu().numpy())
Hi Michael, the very first block of code is not working, the data.zip has not been provided directly, is the shapenet data, the data.zip?
On Thu, 10 Aug, 2023, 9:41 pm Michael A. Alcorn, @.***> wrote:
Closed #8 https://github.com/airalcorn2/pytorch-nerf/issues/8 as completed.
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You need to generate the data yourself using the instructions provided in the README, zip the generated data
folder, and then put that in your own Google Drive.
Thanks, how would you like to be cited?
No citation necessary. You can just link to this repository in any projects that build off of it.
Hi is there a way to train the pixelNerf using this implementation?
The script/notebook trains a pixelNeRF.
Awesome, thanks!
On Mon, 14 Aug, 2023, 10:00 pm Michael A. Alcorn, @.***> wrote:
The script/notebook trains a pixelNeRF.
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@airalcorn2 Can we train custom datasets using any of the approaches you provided?
There seems to a data.zip file for pixel Nerf implementation. Do you mind listing out the steps to get it and more importantly a list of steps to perform inference on a new image, directly from the collab?