(说明:如果您认为下面的文章对您有帮助,请您花费一秒时间点击一下最底部的广告以此来激励本人创作,谢谢!!!)
原始NeRF论文
001 NeRF Representing Scenes as Neural Radiance Fields for View Synthesis
NeRF综述类
002 NEURAL VOLUME RENDERING NERF AND BEYOND
025 Multimodal Image Synthesis and Editing: A Survey
数据集
003 Kubric A scalable dataset generator
144 RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis
快速推理
000 Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
012 Neural Sparse Voxel Fields
028 KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs
035 PlenOctrees for Real-time Rendering of Neural Radiance Fields
036 MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures
039 DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks
044 HeadNeRF: A Real-time NeRF-based Parametric Head Model
053 AutoInt: Automatic Integration for Fast Neural Volume Rendering
057 VaxNeRF: Revisiting the Classic for Voxel-Accelerated Neural Radiance Field
062 153 EfficientNeRF: Efficient Neural Radiance Fields
069 R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis
075 DIVeR: Real-time and Accurate Neural Radiance Fields with Deterministic Integration for Volume Rendering
076 View Synthesis with Sculpted Neural Points
171 360Roam: Real-Time Indoor Roaming Using Geometry-Aware 360 Radiance Fields
160 UNeRF: Time and Memory Conscious U-Shaped Network for Training Neural Radiance Fields
142 SqueezeNeRF: Further factorized FastNeRF for memory-efficient inference
125 NeuSample: Neural Sample Field for Efficient View Synthesis
098 Baking Neural Radiance Fields for Real-Time View Synthesis
097 FastNeRF: High-Fidelity Neural Rendering at 200FPS
093 DeRF: Decomposed Radiance Fields
更多内容请关注公众号:元宇宙MetaAI文章来源:https://www.toymoban.com/news/detail-492994.html
欢迎朋友们投稿,投稿可添加微信:NewYear-2016文章来源地址https://www.toymoban.com/news/detail-492994.html
到了这里,关于计算机视觉与图形学-神经渲染专题-NeRF汇总大礼包-I的文章就介绍完了。如果您还想了解更多内容,请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章,希望大家以后多多支持TOY模板网!