Closed liaopuyun closed 4 months ago
Someone else has successfully run the code, so I believe the script test.py itself is right. Maybe you should check places you changed. The main memory cost is from images loaded in the test dataset, you can load paths instead. But I think this is not the point.
Thank you for your kindly answer.
I carefully checked the code and make sure there is no change.
So I viewed the cuda code in diff-gaussian-rasterization. In the code rasterizer_impl.cu, I found no mater how many gaussian I input, the num_rendered
in line 282 is always 393217, and the binning.sorting_size
in line 192 is always about 20000G.
What is that supposed to mean?
Sorry to say that I have no idea. What about creating the environment again.
Great work! Can't wait to run it on my own data.
I run the script test.py but find it require overmuch CUDA memory when render the gaussian. I try to render with the first 100 points, or decrease the fov by 100 times, yet it makes not much difference. How can I figure out what is going on?