Neural Light Spheres for Implicit Image Stitching and View Synthesis

• URL: http://arxiv.org/abs/2409.17924v2
• Date: Thu, 3 Oct 2024 22:57:06 GMT
• Title: Neural Light Spheres for Implicit Image Stitching and View Synthesis
• Authors: Ilya Chugunov, Amogh Joshi, Kiran Murthy, Francois Bleibel, Felix Heide,
• Abstract summary: Spherical neural light field model for implicit panoramic image stitching and re-rendering. We show improved reconstruction quality over traditional image stitching and radiance field methods.
• Score: 32.396278546192995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Challenging to capture, and challenging to display on a cellphone screen, the panorama paradoxically remains both a staple and underused feature of modern mobile camera applications. In this work we address both of these challenges with a spherical neural light field model for implicit panoramic image stitching and re-rendering; able to accommodate for depth parallax, view-dependent lighting, and local scene motion and color changes during capture. Fit during test-time to an arbitrary path panoramic video capture -- vertical, horizontal, random-walk -- these neural light spheres jointly estimate the camera path and a high-resolution scene reconstruction to produce novel wide field-of-view projections of the environment. Our single-layer model avoids expensive volumetric sampling, and decomposes the scene into compact view-dependent ray offset and color components, with a total model size of 80 MB per scene, and real-time (50 FPS) rendering at 1080p resolution. We demonstrate improved reconstruction quality over traditional image stitching and radiance field methods, with significantly higher tolerance to scene motion and non-ideal capture settings.

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