Point-NeRF: Point-based Neural Radiance Fields

Volumetric neural rendering methods like NeRF [34] generate high-quality view synthesis results but are optimized per-scene leading to prohibitive reconstruction time. On the other hand, deep multi-view stereo methods can quickly reconstruct scene geometry via direct network inference. Point-NeRF co...

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Vydané v:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 5428 - 5438
Hlavní autori: Xu, Qiangeng, Xu, Zexiang, Philip, Julien, Bi, Sai, Shu, Zhixin, Sunkavalli, Kalyan, Neumann, Ulrich
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2022
Predmet:
3D from multi-view and sensors; Computational photography; Image and video synthesis and generation
Pipelines
Point cloud compression
Rendering (computer graphics)
Solid modeling
Surface reconstruction
Three-dimensional displays
Training
ISSN:1063-6919
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Popis
Shrnutí:Volumetric neural rendering methods like NeRF [34] generate high-quality view synthesis results but are optimized per-scene leading to prohibitive reconstruction time. On the other hand, deep multi-view stereo methods can quickly reconstruct scene geometry via direct network inference. Point-NeRF combines the advantages of these two approaches by using neural 3D point clouds, with associated neural features, to model a radiance field. Point-NeRF can be rendered efficiently by aggregating neural point features near scene surfaces, in a ray marching-based rendering pipeline. Moreover, Point-NeRF can be initialized via direct inference of a pre-trained deep network to produce a neural point cloud; this point cloud can be finetuned to surpass the visual quality of NeRF with 30× faster training time. Point-NeRF can be combined with other 3D re-construction methods and handles the errors and outliers in such methods via a novel pruning and growing mechanism.
ISSN:1063-6919
DOI:10.1109/CVPR52688.2022.00536