9 citations · 27 across the 6 of their papers we have counts for
7 papers
TEGLO: High Fidelity Canonical Texture Mapping from Single-View Images
Vishal Vinod, Tanmay Shah, Dmitry Lagun
Recent work in Neural Fields (NFs) learn 3D representations from class-specific single view image collections. However, they are unable to reconstruct the input data preserving hig…
MELON: NeRF with Unposed Images in SO(3)
Axel Levy, Mark Matthews, Matan Sela +2
Neural radiance fields enable novel-view synthesis and scene reconstruction with photorealistic quality from a few images, but require known and accurate camera poses. Conventional…
Attention Beats Concatenation for Conditioning Neural Fields
Daniel Rebain, Mark J. Matthews, Kwang Moo Yi +3
Neural fields model signals by mapping coordinate inputs to sampled values. They are becoming an increasingly important backbone architecture across many fields from vision and gra…
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…
Solving Inverse Problems with NerfGANs
Giannis Daras, Wen-Sheng Chu, Abhishek Kumar +2
We introduce a novel framework for solving inverse problems using NeRF-style generative models. We are interested in the problem of 3-D scene reconstruction given a single 2-D imag…
LOLNeRF: Learn from One Look
Daniel Rebain, Mark Matthews, Kwang Moo Yi +2
We present a method for learning a generative 3D model based on neural radiance fields, trained solely from data with only single views of each object. While generating realistic i…