Comments (3)
Hi, thanks for your interest!
Firstly, please read our code more carefully and you will find we conduct encoding and decoding in line 300-309, which replaces the anchor parameters with the decoded ones (pls refer to the function conduct_decoding
to see this replacement). This encoding and decoding process is conducted at the end of the training process, which is before conducting line 669. Therefore, at the end of training process, the weight
we save for GaussianModel
is the decoded version; so in line 669, the GaussianModel
that we recover is also the same decoded version.
Secondly, I am sorry for any confusion but I am afraid you have some misunderstanding to our method. Once we get hash features, we do not use them to replace pc._anchor_feat
, but use them to build the context and model the entropy of pc._anchor_feat
. Therefore, once pc._anchor_feat
is successfully decoded with the help of grid_encoder in the decoding process (pls refer to the function conduct_decoding
to see this decoding process), the grid_encoder is no longer needed, and the pc._anchor_feat
itself is exactly the decoded version. I recommend you take a more careful read to our paper.
Best
from hac.
Thank you for your reply. I did ignore something important previously.
As to the second issue I raised, I noticed that in generate_neural_gaussians
, the pc._anchor_feat
will still be used to predict the corresponding scale_rot
and color
. That is, during rendering, the feat_context
from grid_encoder
is just context, assisting mlp
s to predict the attributes of neural gaussians. Am I understanding correctly?
from hac.
Hi, yes, you are correct.
from hac.
Related Issues (15)
- 关于存储大小的问题 HOT 13
- I have encountered a fatal error HOT 12
- 如何预览编码后的模型? HOT 2
- Test testing accuracy gap HOT 4
- ModuleNotFoundError: No module named '_gridencoder' HOT 3
- Question of viewer HOT 8
- colab notebook request for inference with custom images
- Undesirable results when using large datasets HOT 2
- CUDA out of memory during rendering process HOT 2
- Distribution of Anchor Attributes
- Building wheel for gridencoder (setup.py) ... error HOT 3
- if you have tested the changes in the use of VRAM for graphics card rendering before and after compression? HOT 3
- Use MipNeRF360 data factor in evaluation. HOT 4
- PSNR HOT 1
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