Here you use the weights of the ray to compute entropy. These weights however are un-normalized, so the calculation of entropy isn't right. I think that either normalizing the weights, or just passing them as logits rather than probs should be correct.

File "/data/ytr/conda/envs/direct/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(input, **kwargs) File "/data/ytr/HashNeRF-pytorch/hash_encoding.py", line 63, in forward voxel_min_vertex, voxel_max_vertex, hashed_voxel_indices = get_voxel_vertices(
File "/data/ytr/HashNeRF-pytorch/utils.py", line 83, in get_voxel_vertices hashed_voxel_indices = hash(voxel_indices, log2_hashmap_size) File "/data/ytr/HashNeRF-pytorch/utils.py", line 18, in hash return ((1<<log2_hashmap_size)-1) & (x73856093 ^ y19349663 ^ z83492791) TypeError: unsupported operand type(s) for &: 'int' and 'Tensor'

Can you tell me how to solve it?

hi, hashNerf works well on synthetic images, however, it doesn't work on nerf_liff_data, such as fern. I run
python3 run_nerf.py --config configs/fern.txt --finest_res 512 --log2_hashmap_size 19 --lrate 0.01 --lrate_decay 10 it do can train. but when it supposed to render, i guess, there's an error. TypeError: unsupported operand type(s) for -: 'numpy.ndarray' and 'Tensor'

and line 169 is

did anybody meet the same error, help me thanks!!! orz

Hello, thank you for releasing this really usefull code! I have been wondering if you and your team have done any test (or think of any integration) with the tiny-cuda-nn bindings already released by NVIDIA. And if, maybe, it could speed-up performance.

Hi @yashbhalgat, Thank you for this great work!

I just inspect the #10, then figure out is caused by the configuration file doesn't have no_batching = True option. I change the code only for the psnr calculation part because I'm afraid that other change might degrade performance. I check that the learning and rendering is OK now.

here is a rendered images after 2000 iteration(again, this is really great work!).

Thanks!

new learner here. 2 questions:

1. what is the suitable weight for tv loss ? (default is 1e-6, I think it is too small)
2. dose official instant-ngp use tv loss?

hi, hashNerf works well on synthetic images, however, it doesn't work on nerf_liff_data, such as fern. I run
python3 run_nerf.py --config configs/fern.txt --finest_res 512 --log2_hashmap_size 19 --lrate 0.01 --lrate_decay 10 it do can train. but when it supposed to render, i guess, there's an error. TypeError: unsupported operand type(s) for -: 'numpy.ndarray' and 'Tensor'

and line 169 is

did anybody meet the same error, help me thanks!!! orz

Hey, Great work on the repo!

Just wanted to ask if there is any straightforward way to load the checkpoint into the original instant-ngp viewer? or if there are any recommendations for generating novel views as one controllably moves around the scene.

Hi, I noticed the hashing is different from the paper, and the prime numbers are also different from the paper. Could you explain what's the meaning of your implementation and the design choice? torch.tensor((1<<log2_hashmap_size)-1) & (x*73856093 ^ y*19349663 ^ z*83492791)

Hello, thank for the work. You have mentioned in the paper that this work is orthogonal to works that leverage hybrid or explicit grid-based representations for view synthesis. (Like instant ngp I believe). Did you made some experiments to combine with their codebase? Does it make sense to go in this direction?

I trained this model on the classroom dataset. It has perfect input view reconstructions but zero generalization. so moving the camera even a tiny step will break the novel view reconstruction. This scene is of size about 10x10x8 meters and the images are omnidirectional. The size of the scene is about 4 times bigger than the synthetic datasets (e.g. the Lego scene) that are used here. Is there something to consider when training on bigger scenes, or is just this model not capable of handing bigger scenes?

Hey there,

I've recently come across your work on NGP via an issue at the official instant-ngp. Great work!

I am working on a very similar project. I have added acceleration structures and dynamic background training in spirit (but not exactly the same) of instant-ngp, however my PSNR results are constantly below the values reported by ngp (on synthetic data: 27dB vs 35dB). Did you experience similar issues?

Here's my repo in case you want to browse it: https://github.com/cheind/pure-torch-ngp

Hey, i am stuck with this error at the function: def trilinear_interp(self, x, voxel_min_vertex, voxel_max_vertex, voxel_embedds) in class HashEmbedder(nn.Module):

RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 2 Emerging at the following line:

c00 = voxel_embedds[:,0]*(1-weights[:,0][:,None]) + voxel_embedds[:,4]*weights[:,0][:,None]

My input was a 3D coordinate, [X, Y, Z], and I do not see where I can reshape or change, because its the voxel embedding that is the problem:

Considering an input with 3 dimensions, and the hash embedding having 2 dimensions, how does this even work? The following is the specs in the function itself:

• x: B x 3
• voxel_min_vertex: B x 3
• voxel_max_vertex: B x 3
• voxel_embedds: B x 8 x 2
• weights = (x - voxel_min_vertex)/(voxel_max_vertex-voxel_min_vertex) # B x 3

Please help!

I am adding a tracking issue for porting over the SDF experiments from InstantNGP.

In case there is any interest or people have tried to implement it.

After training for just 5k iterations (~10 minutes on a single 1050Ti), you start seeing a crisp chair rendering. :).But I trained 5000 times on Tesla M60 6G, which took about an hour in total.Can you help me?