SLAM&Render: A Benchmark for the Intersection Between Neural Rendering, Gaussian Splatting and SLAM

Existing datasets fail to include the specific challenges of two fields: multi-modality and sequentiality in SLAM or generalization across viewpoints and illumination in neural rendering. To bridge this gap, we introduce SLAM&Render, a novel dataset designed to explore the intersection of both domains. It comprises 40 sequences with synchronized RGB, depth, IMU and kinematic-related data. These sequences capture five distinct setups featuring consumer and industrial goods under four different lighting conditions, with separate training and test trajectories per scene, as well as object rearrangements.