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License: Apache License 2.0
Why is there an error after running eval.py:symbol cudaGraphInstantiateWithFlags version libcudart.so.11.0 not defined in file libcudart.so.11.0 with link time reference
Hi,
Thanks for publishing your synthetic dataset and including the kubrics scripts and blender files.
I'd like to do some tunings of the dataset, and I was able to find all the models from shapenet, except for the pillow. May I ask where the pillow model is from and how do you configured it in kubrics?
Thanks
Thanks for sharing this work.
Your paper mentioned that "we do not apply any masks when registering real-world images using COLMAP".
I wonder if your estimated camera poses are good enough for reconstruction, especially when performing SfM on a dynamic scene.
I want to save the rendered result as a ply file. Where should I modify it?
After training for 100000 steps, the static part still has hand shadows. Does it need to be trained more?
Hi! Thanks for releasing this very interesting project and sharing the code! I am wondering why there are five different configs here?
It also seems like skewness
is a sensitive hyperparameters as in the checkpoints you released, all experiments have there own skewness parameter (2.0, 2.75 and 2.875). I am wondering if it's the case.
Thanks!
Hang
If requirements.txt are installed as is, an error is raised during tensorboard import (noting that the protobuf version needs to be downgraded). Could consider explicitly adding e.g. protobuf 3.20.1 to requirements.txt? (Or whichever version was originally intended to be used.)
Dear authors,
Thank you for your excellent work.
I have tested the method on several outdoor scenes involving high ego-motion; however, the results are not entirely satisfactory. Do you believe there might be a gap in adapting to this type of data, or are there adjustments I could make to achieve better separation in such scenarios?
I also played with the hyperparameters but this is the best I can get.
Hi there, I am running your code on some custom videos of mine and for some some reason, when I render the images through the trained NeRF, I can notice some artifact mainly (but not exclusively) concentrated at the borders.
Those artifacts are like random pixels not consistent with the color of the surrounding image and a general increase in blurriness. Let me know if you have encountered the same issues with your experiments and if there is a quick solution to that.
One working theory is that those might be due to imperfect camera poses estimated with COLMAP, but in that case there would be no possible workaround I guess.
Hi, the project is cool but I may need some help with generating novel views. Where should I modify it?
Hi dear authors,
Thanks again for sharing this work. I am wondering have you guys tried comparing against NSFF? It also has decompositing components and seems to work reasonably. Would be a very strong baseline!
Thanks,
Hang
Hi authors, thanks for releasing the code! I am currently facing issues while running this repo on GPU (it's working on CPU but it takes forever). I think the problem is with the required version of jax and cuda support.
Can you please provide more details regarding the CUDA version and NVIDIA drivers that you are using for jax 0.2.26? TIA
Traceback (most recent call last):
File "train.py", line 574, in <module>
app.run(main)
File "/home/wayve/prajwal/d2nerf/env/lib/python3.8/site-packages/absl/app.py", line 312, in run
_run_main(main, args)
File "/home/wayve/prajwal/d2nerf/env/lib/python3.8/site-packages/absl/app.py", line 258, in _run_main
sys.exit(main(argv))
File "train.py", line 495, in main
process_iterator(tag='runtime_eval',
File "train.py", line 528, in process_iterator
model_out = render_fn(state, batch, rng=rng)
File "/home/wayve/prajwal/d2nerf/hypernerf/evaluation.py", line 119, in render_image
chunk_rays_dict = utils.shard(chunk_rays_dict, device_count)
File "/home/wayve/prajwal/d2nerf/hypernerf/utils.py", line 289, in shard
return jax.tree_map(lambda x: x.reshape((device_count, -1) + x.shape[1:]), xs)
File "/home/wayve/prajwal/d2nerf/env/lib/python3.8/site-packages/jax/_src/tree_util.py", line 178, in tree_map
return treedef.unflatten(f(*xs) for xs in zip(*all_leaves))
File "/home/wayve/prajwal/d2nerf/env/lib/python3.8/site-packages/jax/_src/tree_util.py", line 178, in <genexpr>
return treedef.unflatten(f(*xs) for xs in zip(*all_leaves))
File "/home/wayve/prajwal/d2nerf/hypernerf/utils.py", line 289, in <lambda>
return jax.tree_map(lambda x: x.reshape((device_count, -1) + x.shape[1:]), xs)
File "/home/wayve/prajwal/d2nerf/env/lib/python3.8/site-packages/jax/_src/numpy/lax_numpy.py", line 1756, in _reshape
newshape = _compute_newshape(a, args[0] if len(args) == 1 else args)
File "/home/wayve/prajwal/d2nerf/env/lib/python3.8/site-packages/jax/_src/numpy/lax_numpy.py", line 1750, in _compute_newshape
return tuple(- core.divide_shape_sizes(np.shape(a), newshape)
File "/home/wayve/prajwal/d2nerf/env/lib/python3.8/site-packages/jax/_src/numpy/lax_numpy.py", line 1750, in <genexpr>
return tuple(- core.divide_shape_sizes(np.shape(a), newshape)
File "/home/wayve/prajwal/d2nerf/env/lib/python3.8/site-packages/jax/core.py", line 1438, in divide_shape_sizes
return handler.divide_shape_sizes(ds[:len(s1)], ds[len(s1):])
File "/home/wayve/prajwal/d2nerf/env/lib/python3.8/site-packages/jax/core.py", line 1348, in divide_shape_sizes
raise InconclusiveDimensionOperation(f"Cannot divide evenly the sizes of shapes {tuple(s1)} and {tuple(s2)}")
jax.core.InconclusiveDimensionOperation: Cannot divide evenly the sizes of shapes (60, 3) and (8, -1, 3)
I encounter this error when training using 8 GPUs (specifically, the vrig_balloon). The error is raised while trying to render an image during eval.
Here's the corresponding PR that fixes the bug:
#8
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