$\text{NPBG++}$: Accelerating Neural Point-Based Graphics
Ruslan Rakhimov, Andrei-Timotei Ardelean, Victor Lempitsky, Evgeny Vladimirovich Burnaev · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022
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 lever-ages 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 Graph-ics [1] in several important ways. By predicting the descrip-tors 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 suit-able 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. Project page: https://rakhimovv.github.io/npbgpp/.