NPBG++: Accelerating Neural Point-Based Graphics
Ruslan Rakhimov, Andrei-Timotei Ardelean, Victor Lempitsky, Evgeny Burnaev
Abstract
We present a new system (NPBG++) for the novel view synthesis (NVS) task that achieves high rendering realism with low scene fitting time. Our method efficiently leverages the multiview observations and the point cloud of a static scene to predict a neural descriptor for each point, improving upon the pipeline of Neural Point-Based Graphics in several important ways. By predicting the descriptors with a single pass through the source images, we lift the requirement of per-scene optimization while also making the neural descriptors view-dependent and more suitable for scenes with strong non-Lambertian effects. In our comparisons, the proposed system outperforms previous NVS approaches in terms of fitting and rendering runtimes while producing images of similar quality.
BibTeX
@inproceedings{cvpr2022_npbgaccelerating,
title = {NPBG++: Accelerating Neural Point-Based Graphics},
author = {Ruslan Rakhimov and Andrei-Timotei Ardelean and Victor Lempitsky and Evgeny Burnaev},
booktitle = {CVPR 2022},
year = {2022}
}