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

Samuel Cerezo1, Gaetano Meli12, Tomás Berriel1, Kirill Safronov2 and Javier Civera1
1Universidad de Zaragoza, 2KUKA Deutschland GmbH
Submitted to RA-L, 2025
Paper arXiv Code

Abstract

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.

Illustration of our SLAM&Render dataset, captured from a camera in the end effector of a robotic arm, that moves around a set of objects on a table. See four different light conditions present in our dataset: (a)Natural (b)Cold (c)Warm (d)Dark.

Dataset Scenes

Our SLAM&Render dataset includes five distinct scenes, each designed to reflect different types of real-world environments:

  • Setup 1 and Setup 2 feature common supermarket goods, including transparent objects.
  • Setup 3 includes a different selection of supermarket goods (only opaque objects are present).
  • Setup 4 consists of various industrial objects.
  • Setup 5 presents industrial objects in a cluttered arrangement.

You can refer to the visual overview of all setups in the video below

Illumination

Below you can see four different light conditions present in our dataset: Natural - Cold - Warm - Dark.

Download links

Click the links below to download the dataset files (2~3Gb each):

Here you can find the ROS bag files:

BibTeX

@article{slam&render,
  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   = {},
  year      = {2025},
  eprint    = {2504.13713},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url = {https://arxiv.org/abs/2504.13713}, 
}