SILVR: A Synthetic Immersive Large-Volume Plenoptic Dataset

TL;DR

The paper introduces SILVR, a large-volume, panoramic light-field dataset with multi-view images and depth maps, enabling improved 6-DoF navigation and evaluation of light-field rendering techniques.

Contribution

SILVR is the first publicly available large-volume light-field dataset with panoramic views, supporting 6-DoF navigation and providing tools for conversion and rendering evaluation.

Findings

Enables evaluation of light-field coding and rendering techniques.

Supports training of neural radiance fields with large panoramic datasets.

Provides a new benchmark for light-field dataset size and quality.

Abstract

In six-degrees-of-freedom light-field (LF) experiences, the viewer's freedom is limited by the extent to which the plenoptic function was sampled. Existing LF datasets represent only small portions of the plenoptic function, such that they either cover a small volume, or they have limited field of view. Therefore, we propose a new LF image dataset "SILVR" that allows for six-degrees-of-freedom navigation in much larger volumes while maintaining full panoramic field of view. We rendered three different virtual scenes in various configurations, where the number of views ranges from 642 to 2226. One of these scenes (called Zen Garden) is a novel scene, and is made publicly available. We chose to position the virtual cameras closely together in large cuboid and spherical organisations ($2.2m^3$ to $48m^3$), equipped with 180{\deg} fish-eye lenses. Every view is rendered to a color image and depth map of 2048px $\times$ 2048px. Additionally, we present the software used to automate the multi-view rendering process, as well as a lens-reprojection tool that converts between images with panoramic or fish-eye projection to a standard rectilinear (i.e., perspective) projection. Finally, we demonstrate how the proposed dataset and software can be used to evaluate LF coding/rendering techniques(in this case for training NeRFs with instant-ngp). As such, we provide the first publicly-available LF dataset for large volumes of light with full panoramic field of view