mlsanityData to 13 Oct 2026

Out-of-Distribution Detection for Monocular Depth Estimation

arXiv 2308.06072 ↗First posted 11 Aug 2023cs.CVcited by 7

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Abstract (arXiv)

In monocular depth estimation, uncertainty estimation approaches mainly target the data uncertainty introduced by image noise. In contrast to prior work, we address the uncertainty due to lack of knowledge, which is relevant for the detection of data not represented by the training distribution, the so-called out-of-distribution (OOD) data. Motivated by anomaly detection, we propose to detect OOD images from an encoder-decoder depth estimation model based on the reconstruction error. Given the features extracted with the fixed depth encoder, we train an image decoder for image reconstruction using only in-distribution data. Consequently, OOD images result in a high reconstruction error, which we use to distinguish between in- and out-of-distribution samples. We built our experiments on the standard NYU Depth V2 and KITTI benchmarks as in-distribution data. Our post hoc method performs astonishingly well on different models and outperforms existing uncertainty estimation approaches without modifying the trained encoder-decoder depth estimation model.

Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). Builds on 47 of its 59 references that are in the map. First posted on arXiv 11 Aug 2023.

Builds on

47 of 59 references.

References · 47

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,043
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,430
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,902
  4. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,371
  5. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,340
  6. 2021Vision Transformers for Dense Predictioncited by 1,795
  7. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,475
  8. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,327
  9. 2020Energy-based Out-of-distribution Detectioncited by 1,297
  10. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,269
  11. 2018Deep Anomaly Detection with Outlier Exposurecited by 941
  12. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
35 more
  1. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 779
  2. 2020AdaBins: Depth Estimation using Adaptive Binscited by 598
  3. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 513
  4. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  5. 2019Scaling Out-of-Distribution Detection for Real-World Settingscited by 465
  6. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  7. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 405
  8. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 403
  9. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  10. 2017Snapshot Ensembles: Train 1, get M for freecited by 350
  11. 2020Virtual KITTI 2cited by 349
  12. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 339
  13. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  14. 2021MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Spacecited by 233
  15. 2019Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Visioncited by 182
  16. 2020On the uncertainty of self-supervised monocular depth estimationcited by 182
  17. 2021DICE: Leveraging Sparsification for Out-of-Distribution Detectioncited by 166
  18. 2018IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environmentscited by 163
  19. 2022NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimationcited by 151
  20. 2022MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformercited by 136
  21. 2020Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentationcited by 133
  22. 2022Attention Attention Everywhere: Monocular Depth Prediction with Skip Attentioncited by 92
  23. 2018Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometrycited by 85
  24. 2021Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentationcited by 77
  25. 2022Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wildcited by 70
  26. 2021Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environmentscited by 65
  27. 2022Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detectioncited by 59
  28. 2021Neural Mean Discrepancy for Efficient Out-of-Distribution Detectioncited by 42
  29. 2019Robust Anomaly Detection in Images using Adversarial Autoencoderscited by 38
  30. 2022Gradient-based Uncertainty for Monocular Depth Estimationcited by 26
  31. 2022BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networkscited by 23
  32. 2022Out-of-Distribution Detection with Semantic Mismatch under Maskingcited by 22
  33. 2021Bayesian OOD detection with aleatoric uncertainty and outlier exposurecited by 16
  34. 2021Out-of-distribution Detection with Boundary Aware Learningcited by 11
  35. 2022Heatmap-based Out-of-Distribution Detectioncited by 9

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 1.

7 papers.

  1. 2024Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty Estimationcited by 19
  2. 2025Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Modelscited by 9
  3. 2025Revisiting Gradient-based Uncertainty for Monocular Depth Estimationcited by 5
  4. 2024Uni-SLAM: Uncertainty-Aware Neural Implicit SLAM for Real-Time Dense Indoor Scene Reconstructioncited by 3
  5. 2025Uncertainty-Aware Likelihood Ratio Estimation for Pixel-Wise Out-of-Distribution Detectioncited by 3
  6. 2025Latent Uncertainty Representations for Video-based Driver Action and Intention Recognitioncited by 0
  7. 2026RAD: Retrieval-Augmented Monocular Metric Depth Estimation for Underrepresented Classescited by 0