Gaurav Singh
Description: HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork
Neural Radiance Fields (NeRF) have become an increasingly popular representation to capture high-quality appearance and shape of scenes and objects. However, learning generalizable NeRF priors over categories of scenes or objects has been challenging due to the high dimensionality of network weight space. To address the limitations of existing work on generalization, multi-view consistency and to improve quality, we propose HyP-NeRF , a latent conditioning method for learning generalizable category-level Ne
HyP-NeRF is a latent conditioning method for learning improved quality generalizable category-level NeRF priors using hypernetworks. Our hypernetwork is trained to generate the parameters of both, the multi-resolution hash encodings (MRHE) and weights of a NeRF model of a given category conditioned on an instance code. For each instance code, in the learned codebook, HyP-NeRF estimates an instance-specific MRHE along with the weights of an MLP. Our key insight is that estimating both the MRHEs and the weigh
This work was supported by NSF IIS #2143576, NSF CNS #2038897, an STTR Award from Traverse Inc., NSF CloudBank, and an AWS Cloud Credits award.
Gaurav Singh