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Encoding information from 2D views of an object into a 3D representation iscrucial for generalized 3D feature extraction. Such features can then enable 3Dreconstruction, 3D generation, and other applications. We propose GOEmbed(Gradient Origin Embeddings) that encodes input 2D images into any 3Drepresentation, without requiring a pre-trained image feature extractor; unliketypical prior approaches in which input images are either encoded using 2Dfeatures extracted from large pre-trained models, or customized features aredesigned to handle different 3D representations; or worse, encoders may not yetbe available for specialized 3D neural representations such as MLPs andhash-grids. We extensively evaluate our proposed GOEmbed under differentexperimental settings on the OmniObject3D benchmark. First, we evaluate howwell the mechanism compares against prior encoding mechanisms on multiple 3Drepresentations using an illustrative experiment called Plenoptic-Encoding.Second, the efficacy of the GOEmbed mechanism is further demonstrated byachieving a new SOTA FID of 22.12 on the OmniObject3D generation task using acombination of GOEmbed and DFM (Diffusion with Forward Models), which we callGOEmbedFusion. Finally, we evaluate how the GOEmbed mechanism bolsterssparse-view 3D reconstruction pipelines.

GOEmbed: Gradient Origin Embeddings for Representation Agnostic 3D Feature Learning