Papers › Self-Supervised Monocular Depth Hints

Self-Supervised Monocular Depth Hints

19 Sep 2019ICCV 2019 10arXiv:1909.09051archive 2025-07-28

Jamie Watson, Michael Firman, Gabriel J. Brostow, Daniyar Turmukhambetov

Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser scans or other ground-truth data. The disadvantage, however, is that the photometric reprojection losses used with self-supervised learning typically have multiple local minima. These plausible-looking alternatives to ground truth can restrict what a regression network learns, causing it to predict depth maps of limited quality. As one prominent example, depth discontinuities around thin structures are often incorrectly estimated by current state-of-the-art methods. Here, we study the problem of ambiguous reprojections in depth prediction from stereo-based self-supervision, and introduce Depth Hints to alleviate their effects. Depth Hints are complementary depth suggestions obtained from simple off-the-shelf stereo algorithms. These hints enhance an existing photometric loss function, and are used to guide a network to learn better weights. They require no additional data, and are assumed to be right only sometimes. We show that using our Depth Hints gives a substantial boost when training several leading self-supervised-from-stereo models, not just our own. Further, combined with other good practices, we produce state-of-the-art depth predictions on the KITTI benchmark.

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Tasks

Depth EstimationDepth PredictionMonocular Depth EstimationSelf-Supervised Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Monocular Depth Estimation KITTI Eigen split Depth Hints absolute relative error 0.096 #56 of 79 Archive leaderboard report
Monocular Depth Estimation VA (Virtual Apartment) Depth Hints Absolute relative error (AbsRel) 0.197 #2 of 3 Archive leaderboard report
Monocular Depth Estimation VA (Virtual Apartment) Depth Hints Log root mean square error (RMSE_log) 0.248 #2 of 3 Archive leaderboard report
Monocular Depth Estimation VA (Virtual Apartment) Depth Hints Mean average error (MAE) 0.291 #2 of 3 Archive leaderboard report
Monocular Depth Estimation VA (Virtual Apartment) Depth Hints Root mean square error (RMSE) 0.427 #2 of 3 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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