![Google AI Proposes a Computer Vision Framework Called 'LOLNeRF' that Learns to Model 3D Structure and Appearance from Collections of Single-View Images - MarkTechPost Google AI Proposes a Computer Vision Framework Called 'LOLNeRF' that Learns to Model 3D Structure and Appearance from Collections of Single-View Images - MarkTechPost](http://www.marktechpost.com/wp-content/uploads/2022/09/Screen-Shot-2022-09-16-at-10.43.41-AM.png)
Google AI Proposes a Computer Vision Framework Called 'LOLNeRF' that Learns to Model 3D Structure and Appearance from Collections of Single-View Images - MarkTechPost
Neural Fields on Twitter: "NeRF-RPN: A general framework for object detection in NeRFs Authors: Benran Hu, Junkai Huang, Yichen Liu, Yu-Wing Tai, Chi-Keung Tang https://t.co/S5OqDblRwO #neuralfieldsoftheday https://t.co/mFFGrCJpxo" / Twitter
![AK on Twitter: "Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: https://t.co/u9kLfEqYdR Editing a local region or a specific object in a 3D scene represented by a AK on Twitter: "Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: https://t.co/u9kLfEqYdR Editing a local region or a specific object in a 3D scene represented by a](https://pbs.twimg.com/ext_tw_video_thumb/1672060795063259138/pu/img/NwS9yn4zkBab0Uje.jpg:large)
AK on Twitter: "Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: https://t.co/u9kLfEqYdR Editing a local region or a specific object in a 3D scene represented by a
NeRF-Pose Pipeline. Stage 1: Multi-view Neural Object Reconstruction.... | Download Scientific Diagram
GitHub - zju3dv/object_nerf: Code for "Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering", ICCV 2021
![PDF] Template NeRF: Towards Modeling Dense Shape Correspondences from Category-Specific Object Images | Semantic Scholar PDF] Template NeRF: Towards Modeling Dense Shape Correspondences from Category-Specific Object Images | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/35add553d6afb3c30a152d2cd3175c7faba76778/2-Figure2-1.png)