projection-conditioned-point-cloud-diffusion
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lukemelas
Official code for "Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction"
File "/root/projection-conditioned-point-cloud-diffusion/experiments/model/model_coloring.py", line 55, in _forward x_input = self.get_input_with_conditioning(x_input, camera=camera, TypeError: PointCloudProjectionModel.get_input_with_conditioning() missing 1 required positional argument: 't'
Hi, The current sampling codes look contained to the CO3D dataset. Would it be possible to also release the checkpoint and test scripts on the ShapeNet dataset? Thanks in advance!
Hello @lukemelas , I've been exploring your project, and I'm particularly interested in the inference capabilities of your model. Could you provide some information on how long it typically takes...
"/home/sheepsky/miniconda3/envs/pts_diffusion/lib/python3.8/site-packages/accele rate/utils/operations.py", line 171, in send_to_device return type(tensor)( TypeError: __init__() missing 2 required positional arguments: 'sequence_name' and 'sequence_category'
Hi, thanks for your nice work, I want to re-run your project, and I download the tv subset from CO3D and put it under **project_root/datasets** folder like this:  ...
Dear Luke Melas-Kyriazi： Sorry to interrupt you! I'm currently trying to reproduce the results on the shapeNet dataset, but I'm failing at the data loading stage. Specifically, I imitated .json...
The experiment setting is the same as yours.
Dataset
Can you please mention how your dataset looks like. I am trying to implement your work [your code] But something is wrong with the way I downloaded dataset I guess....
Do we have to normalize the color before the color model training? What's the impact of not doing the color normalization?
Hi, congratulations on your fantastic work! I notice that the point cloud renderings in Figure 1-3 of the PC^2 paper look stunning. So I'm wondering how did you render them?...
