- In order to learn a bidirectional mapping between images/text and NeRFS, we train two MLPs, one that maps CLIP image embeddings to \({\tt nf2vec}\) NeRF embeddings, and the other computing the mapping in the opposite direction. + In order to learn a bidirectional mapping between images/text and NeRFS, we train two MLPs, one that maps CLIP image embeddings to \({\tt nf2vec}\) NeRF embeddings, and the other computing the mapping in the opposite direction.
-- We first evaluate the feasibility of using \({\tt inr2vec}\) embeddings of INRs - to solve tasks usually tackled by representation learning, and we select 3D - retrieval as a benchmark. Quantitative results, reported in the following table, - show that, while there is an average gap of 1.8 mAP with PointNet++, \({\tt inr2vec}\) - is able to match, and in some cases even surpass, the performance of the other baselines.