A Straight Line Is Not a Camera Pose: Auditing Short-Clip Pose Benchmarks
Overview
This research abstract presents an audit of camera pose benchmarks, demonstrating that the standard Absolute Trajectory Error (ATE) metric is misleading because it favors constant-velocity predictions regardless of actual geometry. The authors show that for over 95% of model-clip pairs on the Sintel dataset, a simple straight-line prediction yields a lower ATE than complex models, yet performs worse geometrically. They introduce 'pose-anchored ATE,' a metric that incorporates orientation alignment to penalize such artifacts, offering a more robust evaluation method for SLAM and 3D reconstruction systems.
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Reported quantity · median SNR for Sintel: 0.86 other · Basis not reported
“ity prediction onto the best-fitting line through the true path, so the line scores the path's regression residual. A residual signal-to-noise ratio (SNR) criterion predicts when moving a prediction towards its constant-velocity fit lowers ATE. It gives the outcome for 95% of 1056 model-clip pairs, 98% of 792 held-out pairs and 97% of 1034 ScanNet pairs, and it separates Sintel (median SNR 0.86) from TUM, ScanNet and”
Exact source · revision 1Source owner not reported
Reported quantity · cost per clip: 0.17 other · Basis not reported
“ric mean: a lower ATE need not mean better geometry. Fitting the alignment's rotation to the predicted orientations gives pose-anchored ATE, a one-function change in ATE's units costing 0.17 ms per clip (ATE: 0.11 ms). It ranks every image-free reference below every model on Sintel, tracks the reprojection error under rotation and straightening, where ATE does not (Spearman 0.61 vs. 0.32), largely preserves the model”
Exact source · revision 1Source owner not reported
Reported quantity · ATE cost per clip: 0.11 other · Basis not reported
“ric mean: a lower ATE need not mean better geometry. Fitting the alignment's rotation to the predicted orientations gives pose-anchored ATE, a one-function change in ATE's units costing 0.17 ms per clip (ATE: 0.11 ms). It ranks every image-free reference below every model on Sintel, tracks the reprojection error under rotation and straightening, where ATE does not (Spearman 0.61 vs. 0.32), largely preserves the model”
Exact source · revision 1Source owner not reported
Reported quantity · Spearman correlation for ATE under rotation: 0.32 other · Basis not reported
“ric mean: a lower ATE need not mean better geometry. Fitting the alignment's rotation to the predicted orientations gives pose-anchored ATE, a one-function change in ATE's units costing 0.17 ms per clip (ATE: 0.11 ms). It ranks every image-free reference below every model on Sintel, tracks the reprojection error under rotation and straightening, where ATE does not (Spearman 0.61 vs. 0.32), largely preserves the model”
Exact source · revision 1Source owner not reported
Reported quantity · Spearman correlation for pose-anchored ATE under rotation: 0.61 other · Basis not reported
“ric mean: a lower ATE need not mean better geometry. Fitting the alignment's rotation to the predicted orientations gives pose-anchored ATE, a one-function change in ATE's units costing 0.17 ms per clip (ATE: 0.11 ms). It ranks every image-free reference below every model on Sintel, tracks the reprojection error under rotation and straightening, where ATE does not (Spearman 0.61 vs. 0.32), largely preserves the model”
Exact source · revision 1Source owner not reported
Artifact availability · code: reported pending
“s' order, and ties 10-frame translational relative pose error (RPE) and the discernible trajectory error (DTE) on our rotation and reference tests. Code, predictions and results will be released.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalA Straight Line Is Not a Camera Pose: Auditing Short-Clip Pose Benchmarks
This research abstract presents an audit of camera pose benchmarks, demonstrating that the standard Absolute Trajectory Error (ATE) metric is misleading because it favors constant-velocity predictions regardless of actual geometry. The authors show that for over 95% of model-clip pairs on the Sintel dataset, a simple straight-line prediction yields a lower ATE than complex models, yet performs worse geometrically. They introduce 'pose-anchored ATE,' a metric that incorporates orientation alignment to penalize such artifacts, offering a more robust evaluation method for SLAM and 3D reconstruction systems.
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