Comments (4)
Thanks for your suggestions! I will definitely look into it and catch up with these awesome techniques!
from unboundednerfpytorch.
Hi, I'm currently studying instant-ngp as well because currently, we have to rely on some tools from instant-ngp to create interactive scenes. As for development, incorporating instant-ngp is not very easy because it is dependent on CUDA and requires building every time. Besides, it needs a desktop version of GPU but we typically only have headless servers.
from unboundednerfpytorch.
Besides, it needs a desktop version of GPU but we typically only have headless servers.
FYI, I've successfully run renders headlessly as described in the FAQ.
it is dependent on CUDA and requires building every time
This is fair. There's a pytorch port I've got running that doesn't require any compilation but the resulting NeRFs end up with more artifacts than when I run the same collections through instant-ngp. It certainly converges fast though! 😃
from unboundednerfpytorch.
How is it going? I'm also interested in the Block-Nerf with instant-ngp.
from unboundednerfpytorch.
Related Issues (20)
- 运行系统问题
- 运行系统问题 HOT 3
- RuntimeError: Error building extension 'segment_cumsum_cuda' #7 HOT 3
- No module named 'adam_upd_cuda' HOT 5
- 请教 HOT 3
- RuntimeError: Error building extension 'segment_cumsum_cuda' HOT 2
- 训练进程自动退出 HOT 6
- 测试nuscene数据集 HOT 3
- 请教一下运行自己数据的问题 HOT 3
- 训练图片下采样问题 HOT 1
- 渲染视频相机视角问题 HOT 2
- 请问这个训练配置,是对应190G的原始waymo数据, 还是19G的processed 的数据集?
- Export to point clouds HOT 1
- RuntimeError: configuration file type HOT 2
- Could contributor update the latest NeRF information plz HOT 1
- A question about the transformation matrix for San Fran Cisco Mission Bay dataset HOT 1
- How to obtain the appearance embedding? HOT 1
- Question about moving object HOT 2
- A question about the c2w matrix for San Fran Cisco Mission Bay dataset HOT 2
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