TUM RGB-D
Synchronized RGB-D sequences and ground-truth trajectories for evaluating visual odometry and SLAM.
About
TUM RGB-D is compact enough to understand end to end and rich enough to expose the core problems in RGB-D odometry. Its synchronized observations and reference trajectories make it a practical first target for a new SLAM pipeline.
Log estimated poses, reference poses, depth, and reconstructed geometry on one timeline. The combined view makes drift and frame-convention errors visible immediately.
Suggested agent workflows
Copy a prompt into your agent of choice. Each workflow starts small, respects the source terms, and makes the result inspectable in Rerun.
Audit a training slice
Fetch TUM RGB-D from its official source at https://cvg.cit.tum.de/data/datasets/rgbd-dataset. Read and preserve its source terms and citation requirements. Build a reproducible manifest for a representative slice, then load it into Rerun. Inspect the RGB video, Depth, Poses streams for timestamp gaps, missing samples, calibration problems, and annotation inconsistencies. Summarize the findings and save both the validation script and a Rerun recording that makes every issue easy to review.
Build a baseline pipeline
Use TUM RGB-D to scaffold a baseline pipeline for Odometry, SLAM, 3D reconstruction. Start with a small deterministic cohort and document the selection criteria. Create the dataset adapter, preprocessing steps, train and validation splits, and one lightweight baseline. Log inputs, labels, predictions, losses, and representative failures to Rerun so I can inspect the full experiment timeline. Include exact setup and reproduction commands.
Find useful failure cohorts
Analyze TUM RGB-D and propose a practical cohort taxonomy for Odometry, SLAM, 3D reconstruction. Use available metadata and the RGB video, Depth, Poses signals to identify hard cases, rare conditions, coverage gaps, and possible leakage between splits. Materialize the cohorts as queryable metadata, log examples from each cohort to Rerun, and produce a short report explaining which cohorts should become regression tests. Do not infer labels that are not present in the source data.