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I found this repo while looking for a Predictive Coding library, inspired by [this paper](https://arxiv.org/abs/2107.12979). However, the focus here seems to be more on Generalized Hierarchical Gaussi…
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Traceback (most recent call last):
File "train.py", line 280, in
training(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_ite…
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[Graph neural network-inspired kernels for gaussian processes in semi-supervised learning](https://arxiv.org/abs/2302.05828)
```bib
@article{niu2023graph,
title={Graph neural network-inspired ker…
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I'm creating this issue as a place to discuss supporting non-Gaussian likelihoods.
There's been a fair amount of discussion on this for Poisson / Negative Binomial likelihoods (#337). This comes up…
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1. It seems that submodules/diff-gaussian-rasterization is different from [original code](https://github.com/ashawkey/diff-gaussian-rasterization), so you'd better to recompile it using:
```shell
cd…
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Hi, I was working on scaffold-gs and octree-gs which has a mlp for generating neural gaussians on the fly. How can I enable them to be trainable on in distributed way like the vanilla GS?
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Dear author:
Thank you for sharing such a great piece of open-source work!
In `fusion.py`,I have questions about this part of the code：
```
features_mapping = features[:, mapping[:, 1], mapping[:,…
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Under Windows 11
```
D:\Tests\Wild Gaussian Splatting>git clone git@github.com:nerlfield/wild-gaussian-splatting.git --recursive
Cloning into 'wild-gaussian-splatting'...
git@github.com: Permissio…
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Adding a gaussian shape for analogical pulse would make sense. The width can be specified in a different ways : standard deviation, width, etc ... I would be in favor of a switch to select the in put …
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I am trying to export the Gaussians in a ply file. When I open the ply file with a Gaussian viewer (SuperSplat), the Gaussians are too big, as shown in the following image showing the partial reconstr…