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View Code? Open in Web Editor NEWPoint-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast
License: MIT License
Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast
License: MIT License
Thanks for your great work and the release of the code.
I just had a quick look at the different configs and wondered about the input dimensions of the backbone model for pretraining and finetuning.
Whilst the input dimension for pre-training is 64 (dimension of the embedding after a linear layer), the inut dimension for the fine-tuning models is ususally 6 (xyz + rgb).
How does this align?
Additionally, I did not yet fully grasp how inference can actually be done with the code and how the geometry and color features are fused during inference.
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