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README.md

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@@ -35,8 +35,8 @@ using only a single network.
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![network_pipeline_circles](https://github.com/compphoto/Intrinsic/assets/3434597/11462c08-8eee-43c9-b425-a02b47857881)
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We train our method on multiple rendered dataset. In order to generate real-world supervision for our method we use multi-illumination data.
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Using our pipeline we estimate albedo for each image in a given multi-illumination scene. By taking the median across these albedo estimations, small errors are removed resulting in a single accurate albedo.
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We train our method on multiple rendered datasets. In order to generate real-world supervision for our method we use multi-illumination data.
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Using our pipeline we estimate the albedo for each image in a given multi-illumination scene. By taking the median across these albedo estimations, small errors are removed resulting in a single accurate albedo.
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We use these 25,000 pseudo-ground-truth pairs as training data and continue training our pipeline.
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![multi_illum_examples](https://github.com/compphoto/Intrinsic/assets/3434597/05984ff7-f481-4c0c-a156-48eb966a0505)
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```
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pip install https://github.com/compphoto/Intrinsic/archive/master.zip
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```
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This will allow you to import the repository as a python package, and use our pipeline as part of your codebase.
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This will allow you to import the repository as a Python package, and use our pipeline as part of your codebase.
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## Inference
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To run our pipeline on your own images you can use the decompose script:
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```
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```
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![intro_itw_comp_2](https://github.com/compphoto/Intrinsic/assets/3434597/1a25186b-56bb-4bd9-b052-b568ea96ff38)

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