{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/monocular-depth-estimation/papers/9","list_of":"/task/monocular-depth-estimation","task":"Monocular Depth Estimation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":9,"pages_in_order":9,"rows_per_page":100,"rows":[801,876],"of":876,"counts":{"archive_papers_tagged":876,"with_a_code_link":430,"where_syntology_ran_a_sample":136,"not_listed_spam_title":0,"listed":876,"listed_where_code_ran":136,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":120,"every_run_a_failure_of_syntologys_instrument":16,"listed_with_a_run_with_no_instrument_failure":120,"listed_every_run_a_failure_of_syntologys_instrument":16,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/monocular-depth-estimation","prev":"/task/monocular-depth-estimation/papers/8","next":null,"papers":[{"url":null,"slug":"realmonodepth-self-supervised-monocular-depth","title":"RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes","date":"2020-04-14","arxiv_id":"2004.06267","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-depth-estimation-with-self","title":"Monocular Depth Estimation with Self-supervised Instance Adaptation","date":"2020-04-13","arxiv_id":"2004.05821","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attacks-on-monocular-depth","title":"Adversarial Attacks on Monocular Depth Estimation","date":"2020-03-23","arxiv_id":"2003.10315","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-depth-estimation-based-on-deep","title":"Monocular Depth Estimation Based On Deep Learning: An Overview","date":"2020-03-14","arxiv_id":"2003.06620","repositories_listed":0,"syntology":null},{"url":null,"slug":"d3vo-deep-depth-deep-pose-and-deep","title":"D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry","date":"2020-03-02","arxiv_id":"2003.01060","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-decluttering-simplifying-images-to","title":"Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation","date":"2020-02-27","arxiv_id":"2002.12114","repositories_listed":0,"syntology":null},{"url":null,"slug":"dense-monocular-simultaneous-localization-and","title":"Dense monocular Simultaneous Localization and Mapping by direct surfel optimization","date":"2020-02-14","arxiv_id":"1910.01997","repositories_listed":0,"syntology":null},{"url":null,"slug":"fis-nets-full-image-supervised-networks-for","title":"FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation","date":"2020-01-19","arxiv_id":"2001.11092","repositories_listed":0,"syntology":null},{"url":null,"slug":"dont-forget-the-past-recurrent-depth","title":"Don't Forget The Past: Recurrent Depth Estimation from Monocular Video","date":"2020-01-08","arxiv_id":"2001.02613","repositories_listed":0,"syntology":null},{"url":null,"slug":"perception-and-decision-making-of-autonomous","title":"Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey","date":"2020-01-08","arxiv_id":"2001.02319","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-deep-networks-for-monocular-depth","title":"Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method","date":"2019-11-20","arxiv_id":"1911.08790","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-classification-network-for-monocular","title":"Deep Classification Network for Monocular Depth Estimation","date":"2019-10-23","arxiv_id":"1910.10369","repositories_listed":0,"syntology":null},{"url":"/paper/moving-indoor-unsupervised-video-depth","slug":"moving-indoor-unsupervised-video-depth","title":"Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments","date":"2019-10-20","arxiv_id":"1910.08898","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-high-resolution-depth-learning","title":"Unsupervised High-Resolution Depth Learning From Videos With Dual Networks","date":"2019-10-20","arxiv_id":"1910.08897","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-semi-supervised-monocular-depth","title":"Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances","date":"2019-10-04","arxiv_id":"1910.01765","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-3d-pan-via-adaptive-t-shaped","title":"Deep 3D Pan via adaptive \"t-shaped\" convolutions with global and local adaptive dilations","date":"2019-10-02","arxiv_id":"1910.01089","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-depth-from-aberration-map","title":"Deep Depth From Aberration Map","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-piecewise-depth-estimation-in","title":"Monocular Piecewise Depth Estimation in Dynamic Scenes by Exploiting Superpixel Relations","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/structure-attentioned-memory-network-for","slug":"structure-attentioned-memory-network-for","title":"Structure-Attentioned Memory Network for Monocular Depth Estimation","date":"2019-09-10","arxiv_id":"1909.04594","repositories_listed":0,"syntology":null},{"url":"/paper/uasol-a-large-scale-high-resolution-outdoor","slug":"uasol-a-large-scale-high-resolution-outdoor","title":"UASOL, a large-scale high-resolution outdoor stereo dataset","date":"2019-08-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"n-merci-a-new-metric-to-evaluate-the","title":"n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True Error","date":"2019-08-20","arxiv_id":"1908.07253","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-coupled-generative-adversarial","title":"Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation","date":"2019-08-15","arxiv_id":"1908.05794","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-complete-or-to-estimate-that-is-the","title":"To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation","date":"2019-08-15","arxiv_id":"1908.05540","repositories_listed":0,"syntology":null},{"url":"/paper/enhancing-self-supervised-monocular-depth","slug":"enhancing-self-supervised-monocular-depth","title":"Enhancing self-supervised monocular depth estimation with traditional visual odometry","date":"2019-08-08","arxiv_id":"1908.03127","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-adversarial-monocular-depth","title":"Semi-Supervised Adversarial Monocular Depth Estimation","date":"2019-08-06","arxiv_id":"1908.02126","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-view-consistent-learning-for","title":"Adversarial View-Consistent Learning for Monocular Depth Estimation","date":"2019-08-04","arxiv_id":"1908.01301","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-depth-from-monocular-videos-using-2","title":"Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach","date":"2019-07-16","arxiv_id":"1907.06882","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-with-geometric","title":"Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera","date":"2019-07-12","arxiv_id":"1907.05820","repositories_listed":0,"syntology":null},{"url":null,"slug":"slam-endoscopy-enhanced-by-adversarial-depth","title":"SLAM Endoscopy enhanced by adversarial depth prediction","date":"2019-06-29","arxiv_id":"1907.00283","repositories_listed":0,"syntology":null},{"url":"/paper/unsupervised-monocular-depth-and-ego-motion","slug":"unsupervised-monocular-depth-and-ego-motion","title":"Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics","date":"2019-06-12","arxiv_id":"1906.05717","repositories_listed":0,"syntology":null},{"url":"/paper/pattern-affinitive-propagation-across-depth-1","slug":"pattern-affinitive-propagation-across-depth-1","title":"Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation","date":"2019-06-08","arxiv_id":"1906.03525","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-end-to-end-autonomous-driving","title":"Multimodal End-to-End Autonomous Driving","date":"2019-06-07","arxiv_id":"1906.03199","repositories_listed":0,"syntology":null},{"url":null,"slug":"connecting-the-dots-learning-representations","title":"Connecting the Dots: Learning Representations for Active Monocular Depth Estimation","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/monocular-depth-estimation-using-relative","slug":"monocular-depth-estimation-using-relative","title":"Monocular Depth Estimation Using Relative Depth Maps","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/recurrent-neural-network-for-un-supervised-1","slug":"recurrent-neural-network-for-un-supervised-1","title":"Recurrent Neural Network for (Un-)Supervised Learning of Monocular Video Visual Odometry and Depth","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/soft-labels-for-ordinal-regression","slug":"soft-labels-for-ordinal-regression","title":"Soft Labels for Ordinal Regression","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/towards-scene-understanding-unsupervised","slug":"towards-scene-understanding-unsupervised","title":"Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-do-neural-networks-see-depth-in-single","title":"How do neural networks see depth in single images?","date":"2019-05-16","arxiv_id":"1905.07005","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-depth-estimation-with-directional","title":"Monocular Depth Estimation with Directional Consistency by Deep Networks","date":"2019-05-11","arxiv_id":"1905.04467","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-large-rgb-d-dataset-for-semi-supervised","title":"A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation","date":"2019-04-23","arxiv_id":"1904.10230","repositories_listed":0,"syntology":null},{"url":"/paper/deep-optics-for-monocular-depth-estimation","slug":"deep-optics-for-monocular-depth-estimation","title":"Deep Optics for Monocular Depth Estimation and 3D Object Detection","date":"2019-04-18","arxiv_id":"1904.08601","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-monocular-disparity-estimation","title":"A Novel Monocular Disparity Estimation Network with Domain Transformation and Ambiguity Learning","date":"2019-03-20","arxiv_id":"1903.08514","repositories_listed":0,"syntology":null},{"url":null,"slug":"refine-and-distill-exploiting-cycle","title":"Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation","date":"2019-03-11","arxiv_id":"1903.04202","repositories_listed":0,"syntology":null},{"url":"/paper/self-supervised-learning-for-single-view","slug":"self-supervised-learning-for-single-view","title":"Self-supervised Learning for Single View Depth and Surface Normal Estimation","date":"2019-03-01","arxiv_id":"1903.00112","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-depth-estimation-a-survey","title":"Monocular Depth Estimation: A Survey","date":"2019-01-27","arxiv_id":"1901.09402","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-monocular-stereo-matching","title":"Unsupervised monocular stereo matching","date":"2018-12-31","arxiv_id":"1812.11671","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-learning-of-monocular-depth","title":"Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss","date":"2018-12-08","arxiv_id":"1812.03368","repositories_listed":0,"syntology":null},{"url":null,"slug":"double-refinement-network-for-efficient","title":"Double Refinement Network for Efficient Indoor Monocular Depth Estimation","date":"2018-11-20","arxiv_id":"1811.08466","repositories_listed":0,"syntology":null},{"url":null,"slug":"playing-for-depth","title":"Playing for Depth","date":"2018-10-15","arxiv_id":"1810.06268","repositories_listed":0,"syntology":null},{"url":"/paper/superdepth-self-supervised-super-resolved","slug":"superdepth-self-supervised-super-resolved","title":"SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation","date":"2018-10-03","arxiv_id":"1810.01849","repositories_listed":0,"syntology":null},{"url":null,"slug":"merci-a-new-metric-to-evaluate-the","title":"MERCI: A NEW METRIC TO EVALUATE THE CORRELATION BETWEEN PREDICTIVE UNCERTAINTY AND TRUE ERROR","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"eliminating-the-blind-spot-adapting-3d-object","title":"Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360Â° Panoramic Imagery","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/joint-task-recursive-learning-for-semantic","slug":"joint-task-recursive-learning-for-semantic","title":"Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"look-deeper-into-depth-monocular-depth","title":"Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-depth-estimation-using-whole-strip","title":"Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-depth-estimation-with-affinity","title":"Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-monocular-depth-estimation-with","title":"Rethinking Monocular Depth Estimation with Adversarial Training","date":"2018-08-22","arxiv_id":"1808.07528","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-scale-architectures-for-monocular","title":"Deep multi-scale architectures for monocular depth estimation","date":"2018-06-08","arxiv_id":"1806.03051","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-depth-estimation-with-augmented","title":"Monocular Depth Estimation with Augmented Ordinal Depth Relationships","date":"2018-06-02","arxiv_id":"1806.00585","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-with-cinematic-rendering-fine","title":"Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images","date":"2018-05-22","arxiv_id":"1805.08400","repositories_listed":0,"syntology":null},{"url":"/paper/monocular-depth-estimation-by-learning-from","slug":"monocular-depth-estimation-by-learning-from","title":"Monocular Depth Estimation by Learning from Heterogeneous Datasets","date":"2018-03-21","arxiv_id":"1803.08018","repositories_listed":0,"syntology":null},{"url":"/paper/adadepth-unsupervised-content-congruent","slug":"adadepth-unsupervised-content-congruent","title":"AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation","date":"2018-03-05","arxiv_id":"1803.01599","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-relative-depth-learning-for","title":"Self-Supervised Relative Depth Learning for Urban Scene Understanding","date":"2017-12-13","arxiv_id":"1712.04850","repositories_listed":0,"syntology":null},{"url":null,"slug":"aperture-supervision-for-monocular-depth","title":"Aperture Supervision for Monocular Depth Estimation","date":"2017-11-21","arxiv_id":"1711.07933","repositories_listed":0,"syntology":null},{"url":null,"slug":"flame-fast-lightweight-mesh-estimation-using","title":"FLaME: Fast Lightweight Mesh Estimation Using Variational Smoothing on Delaunay Graphs","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-compromise-principle-in-deep-monocular","title":"A Compromise Principle in Deep Monocular Depth Estimation","date":"2017-08-28","arxiv_id":"1708.08267","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-deep-learning-for-monocular","title":"Semi-Supervised Deep Learning for Monocular Depth Map Prediction","date":"2017-02-09","arxiv_id":"1702.02706","repositories_listed":0,"syntology":null},{"url":"/paper/researchdoom-and-cocodoom-learning-computer","slug":"researchdoom-and-cocodoom-learning-computer","title":"ResearchDoom and CocoDoom: Learning Computer Vision with Games","date":"2016-10-07","arxiv_id":"1610.02431","repositories_listed":0,"syntology":null},{"url":"/paper/a-two-streamed-network-for-estimating-fine","slug":"a-two-streamed-network-for-estimating-fine","title":"A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images","date":"2016-07-04","arxiv_id":"1607.00730","repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-estimation-from-single-image-using","title":"Depth Estimation from Single Image using Sparse Representations","date":"2016-06-27","arxiv_id":"1606.08315","repositories_listed":0,"syntology":null},{"url":null,"slug":"dense-monocular-depth-estimation-in-complex","title":"Dense Monocular Depth Estimation in Complex Dynamic Scenes","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-depth-estimation-using-neural","title":"Monocular Depth Estimation Using Neural Regression Forest","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-from-a-single-image-by-harmonizing","title":"Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions","date":"2016-05-23","arxiv_id":"1605.07081","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-depth-prediction-in-challenging","title":"Structured Depth Prediction in Challenging Monocular Video Sequences","date":"2015-11-19","arxiv_id":"1511.06070","repositories_listed":0,"syntology":null},{"url":null,"slug":"hc-search-for-structured-prediction-in","title":"HC-Search for Structured Prediction in Computer Vision","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discrete-continuous-depth-estimation-from-a","title":"Discrete-Continuous Depth Estimation from a Single Image","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"56c5ebef1b30059e4ae861e77c2f7d092e83ee2d09e2ce82ca06593059a9209e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}