KITTI Odometry
A foundational driving benchmark for visual odometry, stereo, optical flow, and 3D perception.
About
KITTI remains a useful compact benchmark for geometry and motion pipelines. Its familiar sequences make it practical for validating a new loader, coordinate transform, or odometry baseline before moving to a much larger corpus.
Visualize predicted trajectories beside reference poses and camera observations to catch drift, frame convention mistakes, and sequence-specific failures.
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 KITTI Odometry from its official source at https://www.cvlibs.net/datasets/kitti/. Read and preserve its source terms and citation requirements. Build a reproducible manifest for a representative slice, then load it into Rerun. Inspect the Camera, Lidar, 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 KITTI Odometry to scaffold a baseline pipeline for Odometry, 3D detection, Depth. 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 KITTI Odometry and propose a practical cohort taxonomy for Odometry, 3D detection, Depth. Use available metadata and the Camera, Lidar, 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.