SOMSI: Spherical Novel View Synthesis with Soft Occlusion Multi-Sphere ImagesTewodros Amberbir Habtegebrial; Christiano Couto Gava; Marcel Rogge; Didier Stricker; Varun Jampani
In: International Conference on Computer Vision and Pattern Recognition (CVPR) 2022. International Conference on Computer Vision and Pattern Recognition (CVPR-2022), June 19-24, New Orleans, Louisiana, USA, Computer Vision Foundation, 6/2022.
Spherical novel view synthesis (SNVS) is the task of estimating 360 views at dynamic novel views given a set of 360 input views. Prior arts learn multi-sphere image (MSI) representations that enables fast rendering times but are only limited to modelling low-dimensional color values. Modelling high-dimensional appearance features in MSI can result in better view synthesis, but it is not feasible to represent high-dimensional features in a large number (>64) of MSI spheres. We propose a novel MSI representation called Soft Occlusion MSI (SOMSI) that enables modelling high-dimensional appearance features in MSI while retaining the fast rendering times of a standard MSI. Our key insight is to model appearance features in a smaller set (e.g. 3) of occlusion levels instead of larger number of MSI levels. Experiments on both synthetic and real-world scenes demonstrate that using SOMSI can provide a good balance between accuracy and runtime. SOMSI can produce considerably better results compared to MSI based MODS, while having similar fast rendering time. SOMSI view synthesis quality is on-par with state-of-the-art NeRF like model while being 2 orders of magnitude faster. For code, additional results and data, please visit https://tedyhabtegebrial.github.io/somsi.