Model, decoder, and feature data for Cross-cue reconstruction of perceived 3D object structure from human visual cortex
Abstract
Overview This item contains model-related data used in the following paper: "Cross-cue reconstruction of perceived 3D object structure from human visual cortex." In preparation. The data include trained point-cloud autoencoder parameters, DNN latent features extracted from model-input point-cloud representations, and trained fMRI-to-feature decoders used in the reconstruction pipeline. AtlasNet is the primary autoencoder used for the main analyses, and a diffusion-based point-cloud autoencoder is included as a complementary model used for robustness checks. Source materials note This item distributes model parameters, DNN feature arrays, and fMRI-to-feature decoder parameters. Some files in this item were trained from, extracted from, or otherwise derived from point-cloud representations associated with third-party 3D object resources, including ShapeNet and 3D Warehouse resources used in the study. Accordingly, reuse of these files in relation to the underlying 3D object resources may be subject to the ShapeNet Terms of Use, the 3D Warehouse Terms of Use, and any applicable rights retained by the original model developers or other rights holders. Users are responsible for ensuring that their use of these files complies with all applicable terms and rights. The item-level license displayed by Figshare does not supersede or replace the terms and restrictions associated with the underlying third-party 3D model resources. It should not be interpreted as granting unrestricted…
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