atlas

Research map / Label-efficient and robust learning / Self-supervised and contrastive

On the Uncertainty of Self-Supervised Monocular Depth Estimation

CVPR 2020 · Label-efficient and robust learning › Self-supervised and contrastive · approach: Pretext-task visual pre-training · 278 citations (OpenAlex)

Check your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add Self-Teaching by training a student to predict the teacher's depth distributionMonodepth2 with monocular supervision · KITTI Eigen splitAbs Rel: 0.090 -> 0.087Helped
Add Self-Teaching by training a student to predict the teacher's depth distributionMonodepth2 with monocular supervision · KITTI Eigen splitAUSE (RMSE): 2.562 -> 2.009Helped
Add Self-Teaching by training a student to predict the teacher's depth distributionMonodepth2 with stereo supervision · KITTI Eigen splitAbs Rel: 0.085 -> 0.084Helped
Use log-likelihood uncertainty instead of Self-TeachingMonodepth2 with stereo supervision · KITTI Eigen splitAUSE (RMSE): 1.679 -> 0.938Helped
Add Self-Teaching by training a student to predict the teacher's depth distributionMonodepth2 with monocular+stereo supervision · KITTI Eigen splitRMSE: 3.739 -> 3.682Helped
Add Self-Teaching rather than log-likelihood uncertaintyMonodepth2 with monocular+stereo supervision · KITTI Eigen splitAUSE (RMSE): 1.714 -> 1.654Helped
Combine bootstrapped ensembles with Self-Teaching (ablation)Monodepth2 with monocular supervision · KITTI Eigen splitAUSE (RMSE): 2.009 -> 1.924Helped
Use dropout sampling for uncertainty estimationMonodepth2 with stereo supervision · KITTI Eigen splitAURG (RMSE): -2.169Hurt
Use dropout sampling for uncertainty estimationMonodepth2 with monocular supervision · KITTI Eigen splitAbs Rel: 0.090 -> 0.101Hurt
Use log-likelihood uncertainty with estimated pose rather than Self-TeachingMonodepth2 with monocular supervision · KITTI Eigen splitAUSE (RMSE): 2.009 -> 2.562Hurt
Combine empirical estimation with predictive uncertainty estimation (ablation)Monodepth2 under monocular supervision · KITTI Eigen splitAUSE (RMSE): 2.562 -> 1.924 (Boot+Self); 2.562 -> 2.449 (Boot+Log)Mixed

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy