9 citations · 9 across the 4 of their papers we have counts for
5 papers
Marginalized Bundle Adjustment: Multi-View Camera Pose from Monocular Depth Estimates
Shengjie Zhu, Ahmed Abdelkader, Mark J. Matthews +2
Structure-from-Motion (SfM) is a fundamental 3D vision task for recovering camera parameters and scene geometry from multi-view images. While recent deep learning advances enable a…
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…
CUF: Continuous Upsampling Filters
Cristina Vasconcelos, Cengiz Oztireli, Mark Matthews +3
Neural fields have rapidly been adopted for representing 3D signals, but their application to more classical 2D image-processing has been relatively limited. In this paper, we cons…
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…
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…