Models and methods originally developed for novel view synthesis and scene rendering, such as Neural Radiance Fields (NeRF) and Gaussian Splatting, are increasingly being adopted as representations in Simultaneous Localization and Mapping (SLAM). However, existing datasets fail to include the specific challenges of both fields, such as multimodality and sequentiality in SLAM or generalization across viewpoints and illumination conditions in neural rendering. To bridge this gap, we introduce SLAM&Render, a novel dataset designed to benchmark methods in the intersection between SLAM and novel view rendering. It consists of 40 sequences with synchronized RGB, depth, IMU, robot kinematic data, and ground-truth pose streams. By releasing robot kinematic data, the dataset also enables the assessment of novel SLAM strategies when applied to robot manipulators. The dataset sequences span five different setups featuring consumer and industrial objects under four different lighting conditions, with separate training and test trajectories per scene, as well as object rearrangements. Our experimental results, obtained with several baselines from the literature, validate SLAM&Render as a relevant benchmark for this emerging research area.
View on arXiv@article{cerezo2025_2504.13713,
title={ SLAM&Render: A Benchmark for the Intersection Between Neural Rendering, Gaussian Splatting and SLAM },
author={ Samuel Cerezo and Gaetano Meli and Tomás Berriel Martins and Kirill Safronov and Javier Civera },
journal={arXiv preprint arXiv:2504.13713},
year={ 2025 }
}