accelerate
Papers with tag accelerate
2022
2021
- Geometry-Guided Progressive NeRF for Generalizable and Efficient Neural Human RenderingMingfei Chen, Jianfeng Zhang, Xiangyu Xu, Lijuan Liu, Yujun Cai, Jiashi Feng, and Shuicheng YanIn ECCV 2021
In this work we develop a generalizable and efficient Neural Radiance Field(NeRF) pipeline for high-fidelity free-viewpoint human body synthesis undersettings with sparse camera views. Though existing NeRF-based methods cansynthesize rather realistic details for human body, they tend to produce poorresults when the input has self-occlusion, especially for unseen humans undersparse views. Moreover, these methods often require a large number of samplingpoints for rendering, which leads to low efficiency and limits their real-worldapplicability. To address these challenges, we propose a Geometry-guidedProgressive NeRF (GP-NeRF). In particular, to better tackle self-occlusion, wedevise a geometry-guided multi-view feature integration approach that utilizesthe estimated geometry prior to integrate the incomplete information from inputviews and construct a complete geometry volume for the target human body.Meanwhile, for achieving higher rendering efficiency, we introduce aprogressive rendering pipeline through geometry guidance, which leverages thegeometric feature volume and the predicted density values to progressivelyreduce the number of sampling points and speed up the rendering process.Experiments on the ZJU-MoCap and THUman datasets show that our methodoutperforms the state-of-the-arts significantly across multiple generalizationsettings, while the time cost is reduced > 70% via applying our efficientprogressive rendering pipeline.
Geometry-guided image feature integration获得density volume,减少采样的点的数量