See360: Novel Panoramic View Interpolation
Zhi-Song Liu Marie-Paule Cani Wan-Chi Siu
We present See360, which is a versatile and efficient framework for 360
panoramic view interpolation using latent space viewpoint estimation. Most of
the existing view rendering approaches only focus on indoor or synthetic 3D
environments and render new views of small objects. In contrast, we suggest to
tackle camera-centered view synthesis as a 2D affine transformation without
using point clouds or depth maps, which enables an effective 360? panoramic
scene exploration. Given a pair of reference images, the See360 model learns to
render novel views by a proposed novel Multi-Scale Affine Transformer (MSAT),
enabling the coarse-to-fine feature rendering. We also propose a Conditional
Latent space AutoEncoder (C-LAE) to achieve view interpolation at any arbitrary
angle. To show the versatility of our method, we introduce four training
datasets, namely UrbanCity360, Archinterior360, HungHom360 and Lab360, which
are collected from indoor and outdoor environments for both real and synthetic
rendering. Experimental results show that the proposed method is generic enough
to achieve real-time rendering of arbitrary views for all four datasets. In
addition, our See360 model can be applied to view synthesis in the wild: with
only a short extra training time (approximately 10 mins), and is able to render
unknown real-world scenes. The superior performance of See360 opens up a
promising direction for camera-centered view rendering and 360 panoramic view
interpolation.