{"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/depth-estimation/papers/24","list_of":"/task/depth-estimation","task":"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":24,"pages_in_order":25,"rows_per_page":100,"rows":[2301,2400],"of":2454,"counts":{"archive_papers_tagged":2454,"with_a_code_link":1029,"where_syntology_ran_a_sample":292,"not_listed_spam_title":0,"listed":2454,"listed_where_code_ran":292,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":260,"every_run_a_failure_of_syntologys_instrument":32,"listed_with_a_run_with_no_instrument_failure":260,"listed_every_run_a_failure_of_syntologys_instrument":32,"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/depth-estimation","prev":"/task/depth-estimation/papers/23","next":"/task/depth-estimation/papers/25","papers":[{"url":null,"slug":"deeplens-shallow-depth-of-field-from-a-single","title":"DeepLens: Shallow Depth Of Field From A Single Image","date":"2018-10-18","arxiv_id":"1810.08100","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":"disnet-a-novel-method-for-distance-estimation","title":"DisNet: A novel method for distance estimation from monocular camera","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"boundary-guided-feature-aggregation-network","title":"Boundary-guided Feature Aggregation Network for Salient Object Detection","date":"2018-09-28","arxiv_id":"1809.10821","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-dynamic-object-detection-for","title":"Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps","date":"2018-09-28","arxiv_id":"1809.11036","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":"ganvo-unsupervised-deep-monocular-visual","title":"GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks","date":"2018-09-16","arxiv_id":"1809.05786","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-depth-from-motion-with-stabilized","title":"End-to-end depth from motion with stabilized monocular videos","date":"2018-09-12","arxiv_id":"1809.04453","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-structure-from-motion-from-motion","title":"Learning structure-from-motion from motion","date":"2018-09-12","arxiv_id":"1809.04471","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-relationship-prediction-via-label","title":"Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information","date":"2018-09-09","arxiv_id":"1809.02945","repositories_listed":0,"syntology":null},{"url":null,"slug":"detail-preserving-depth-estimation-from-a","title":"Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks","date":"2018-09-03","arxiv_id":"1809.00646","repositories_listed":0,"syntology":null},{"url":"/paper/distortion-aware-convolutional-filters-for","slug":"distortion-aware-convolutional-filters-for","title":"Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images","date":"2018-09-01","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":null,"slug":"end-to-end-view-synthesis-for-light-field","title":"End-to-end View Synthesis for Light Field Imaging with Pseudo 4DCNN","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"into-the-twilight-zone-depth-estimation-using","title":"Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization","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":"a-deeper-insight-into-the-undemon","title":"A Deeper Insight into the UnDEMoN: Unsupervised Deep Network for Depth and Ego-Motion Estimation","date":"2018-08-27","arxiv_id":"1809.00969","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":"simultaneous-localization-and-mapping-with","title":"Simultaneous Localization And Mapping with depth Prediction using Capsule Networks for UAVs","date":"2018-08-16","arxiv_id":"1808.05336","repositories_listed":0,"syntology":null},{"url":null,"slug":"cream-condensed-real-time-models-for-depth","title":"CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks","date":"2018-07-24","arxiv_id":"1807.08931","repositories_listed":0,"syntology":null},{"url":null,"slug":"peeking-behind-objects-layered-depth","title":"Peeking Behind Objects: Layered Depth Prediction from a Single Image","date":"2018-07-23","arxiv_id":"1807.08776","repositories_listed":0,"syntology":null},{"url":null,"slug":"eng-end-to-end-neural-geometry-for-robust","title":"ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs","date":"2018-07-16","arxiv_id":"1807.05705","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-virtual-stereo-odometry-leveraging-deep","title":"Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry","date":"2018-07-06","arxiv_id":"1807.02570","repositories_listed":0,"syntology":null},{"url":"/paper/every-pixel-counts-unsupervised-geometry","slug":"every-pixel-counts-unsupervised-geometry","title":"Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding","date":"2018-06-27","arxiv_id":"1806.10556","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-single-image-depth-from-videos-using","title":"Learning Single-Image Depth from Videos using Quality Assessment Networks","date":"2018-06-25","arxiv_id":"1806.09573","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-dense-depth-1","title":"Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy","date":"2018-06-25","arxiv_id":"1806.09521","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":"soccer-on-your-tabletop","title":"Soccer on Your Tabletop","date":"2018-06-03","arxiv_id":"1806.00890","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":"free-supervision-from-video-games","title":"Free Supervision From Video Games","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"matching-adversarial-networks","title":"Matching Adversarial Networks","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/monocular-relative-depth-perception-with-web","slug":"monocular-relative-depth-perception-with-web","title":"Monocular Relative Depth Perception With Web Stereo Data Supervision","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"trust-your-model-light-field-depth-estimation","title":"Trust Your Model: Light Field Depth Estimation With Inline Occlusion Handling","date":"2018-06-01","arxiv_id":null,"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":null,"slug":"recurrent-neural-network-for-learning","title":"Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video","date":"2018-05-17","arxiv_id":"1805.06558","repositories_listed":0,"syntology":null},{"url":null,"slug":"just-in-time-reconstruction-inpainting-sparse","title":"Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors","date":"2018-05-11","arxiv_id":"1805.04239","repositories_listed":0,"syntology":null},{"url":"/paper/pad-net-multi-tasks-guided-prediction-and","slug":"pad-net-multi-tasks-guided-prediction-and","title":"PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing","date":"2018-05-11","arxiv_id":"1805.04409","repositories_listed":0,"syntology":null},{"url":null,"slug":"position-estimation-of-camera-based-on","title":"Position Estimation of Camera Based on Unsupervised Learning","date":"2018-05-05","arxiv_id":"1805.02020","repositories_listed":0,"syntology":null},{"url":"/paper/evaluation-of-cnn-based-single-image-depth","slug":"evaluation-of-cnn-based-single-image-depth","title":"Evaluation of CNN-based Single-Image Depth Estimation Methods","date":"2018-05-03","arxiv_id":"1805.01328","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-cross-domain-building-extraction-for","title":"Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery","date":"2018-04-23","arxiv_id":"1804.08302","repositories_listed":0,"syntology":null},{"url":null,"slug":"motion-guided-lidar-camera-autocalibration","title":"Motion Guided LIDAR-camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles","date":"2018-03-28","arxiv_id":"1803.10681","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-depth-from-single-images-with-deep","title":"Learning Depth from Single Images with Deep Neural Network Embedding Focal Length","date":"2018-03-27","arxiv_id":"1803.10039","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":null,"slug":"robust-depth-estimation-from-auto-bracketed","title":"Robust Depth Estimation from Auto Bracketed Images","date":"2018-03-21","arxiv_id":"1803.07702","repositories_listed":0,"syntology":null},{"url":null,"slug":"fusion-of-stereo-and-still-monocular-depth","title":"Fusion of stereo and still monocular depth estimates in a self-supervised learning context","date":"2018-03-20","arxiv_id":"1803.07512","repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-fisheye-camera-depth-estimation","title":"Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision","date":"2018-03-16","arxiv_id":"1803.06192","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-monocular-image-depth","title":"Self-Supervised Monocular Image Depth Learning and Confidence Estimation","date":"2018-03-14","arxiv_id":"1803.05530","repositories_listed":0,"syntology":null},{"url":null,"slug":"driving-scene-perception-network-real-time","title":"Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation","date":"2018-03-10","arxiv_id":"1803.03778","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":"convolutional-neural-network-based-regression","title":"Convolutional neural network-based regression for depth prediction in digital holography","date":"2018-02-02","arxiv_id":"1802.00664","repositories_listed":0,"syntology":null},{"url":"/paper/joint-voxel-and-coordinate-regression-for","slug":"joint-voxel-and-coordinate-regression-for","title":"Joint Voxel and Coordinate Regression for Accurate 3D Facial Landmark Localization","date":"2018-01-28","arxiv_id":"1801.09242","repositories_listed":0,"syntology":null},{"url":null,"slug":"size-to-depth-a-new-perspective-for-single","title":"Size-to-depth: A New Perspective for Single Image Depth Estimation","date":"2018-01-13","arxiv_id":"1801.04461","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":"joint-blind-motion-deblurring-and-depth","title":"Joint Blind Motion Deblurring and Depth Estimation of Light Field","date":"2017-11-29","arxiv_id":"1711.10918","repositories_listed":0,"syntology":null},{"url":null,"slug":"entropy-difference-based-stereo-error","title":"Entropy-difference based stereo error detection","date":"2017-11-28","arxiv_id":"1711.10412","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":"total-variation-based-dense-depth-from-multi","title":"Total Variation-Based Dense Depth from Multi-Camera Array","date":"2017-11-21","arxiv_id":"1711.07719","repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-assisted-full-resolution-network-for","title":"Depth Assisted Full Resolution Network for Single Image-based View Synthesis","date":"2017-11-17","arxiv_id":"1711.06620","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-reverse-domain-adaptation-for","title":"Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training","date":"2017-11-17","arxiv_id":"1711.06606","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-deeply-supervised-good-features-to","title":"Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction","date":"2017-11-16","arxiv_id":"1711.05919","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-learning-of-geometry-with-edge","title":"Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency","date":"2017-11-10","arxiv_id":"1711.03665","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-camera-focus-estimation-for-gaze-based","title":"Fast camera focus estimation for gaze-based focus control","date":"2017-11-09","arxiv_id":"1711.03306","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-depth-estimation-using-mask-based","title":"Toward Depth Estimation Using Mask-Based Lensless Cameras","date":"2017-11-09","arxiv_id":"1711.03527","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-and-conditional-random-fields","title":"Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy","date":"2017-10-30","arxiv_id":"1710.11216","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-adaptive-framework-for-missing-depth","title":"An Adaptive Framework for Missing Depth Inference Using Joint Bilateral Filter","date":"2017-10-14","arxiv_id":"1710.05221","repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-estimation-using-structured-light-flow","title":"Depth estimation using structured light flow -- analysis of projected pattern flow on an object's surface --","date":"2017-10-02","arxiv_id":"1710.00513","repositories_listed":0,"syntology":null},{"url":null,"slug":"composite-focus-measure-for-high-quality","title":"Composite Focus Measure for High Quality Depth Maps","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-and-image-restoration-from-light-field","title":"Depth and Image Restoration From Light Field in a Scattering Medium","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-estimation-using-structured-light-flow-1","title":"Depth Estimation Using Structured Light Flow -- Analysis of Projected Pattern Flow on an Object's Surface","date":"2017-10-01","arxiv_id":null,"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":"low-compute-and-fully-parallel-computer","title":"Low Compute and Fully Parallel Computer Vision With HashMatch","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"monocular-3d-human-pose-estimation-by","title":"Monocular 3D Human Pose Estimation by Predicting Depth on Joints","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-pseudo-random-fields-for-light-field","title":"Robust Pseudo Random Fields for Light-Field Stereo Matching","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"scale-recovery-for-monocular-visual-odometry","title":"Scale Recovery for Monocular Visual Odometry Using Depth Estimated With Deep Convolutional Neural Fields","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"j-mod2-joint-monocular-obstacle-detection-and","title":"J-MOD$^{2}$: Joint Monocular Obstacle Detection and Depth Estimation","date":"2017-09-25","arxiv_id":"1709.08480","repositories_listed":0,"syntology":null},{"url":null,"slug":"exact-blur-measure-outperforms-conventional","title":"Exact Blur Measure Outperforms Conventional Learned Features for Depth Finding","date":"2017-08-31","arxiv_id":"1709.00072","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":"/paper/multi-task-self-supervised-visual-learning","slug":"multi-task-self-supervised-visual-learning","title":"Multi-task Self-Supervised Visual Learning","date":"2017-08-25","arxiv_id":"1708.07860","repositories_listed":0,"syntology":null},{"url":null,"slug":"reflection-separation-and-deblurring-of","title":"Reflection Separation and Deblurring of Plenoptic Images","date":"2017-08-22","arxiv_id":"1708.06779","repositories_listed":0,"syntology":null},{"url":null,"slug":"splode-semi-probabilistic-point-and-line","title":"SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion","date":"2017-08-09","arxiv_id":"1708.02837","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-light-field-depth-estimation-with","title":"Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions","date":"2017-08-07","arxiv_id":"1708.01964","repositories_listed":0,"syntology":null},{"url":null,"slug":"occlusion-handling-using-semantic","title":"Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality","date":"2017-07-30","arxiv_id":"1707.09603","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-and-angular-resolution-enhancement-of","title":"Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks","date":"2017-07-04","arxiv_id":"1707.00815","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-wide-field-of-view-monocentric-light-field","title":"A Wide-Field-Of-View Monocentric Light Field Camera","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-siamese-learning-on-stereo","title":"Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery","date":"2017-05-17","arxiv_id":"1705.08260","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-focus-learning-depth-from-image-bokeh","title":"Out-of-focus: Learning Depth from Image Bokeh for Robotic Perception","date":"2017-05-02","arxiv_id":"1705.01152","repositories_listed":0,"syntology":null},{"url":null,"slug":"ocrapose-ii-an-ocr-based-indoor-positioning","title":"OCRAPOSE II: An OCR-based indoor positioning system using mobile phone images","date":"2017-04-19","arxiv_id":"1704.05591","repositories_listed":0,"syntology":null},{"url":null,"slug":"surface-normals-in-the-wild","title":"Surface Normals in the Wild","date":"2017-04-10","arxiv_id":"1704.02956","repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-from-monocular-images-using-a-semi","title":"Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture","date":"2017-03-10","arxiv_id":"1703.03867","repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-estimation-using-modified-cost-function","title":"Depth Estimation using Modified Cost Function for Occlusion Handling","date":"2017-03-02","arxiv_id":"1703.00919","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-modular-cnn-architectures-for-joint","title":"Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation","date":"2017-02-26","arxiv_id":"1702.08009","repositories_listed":0,"syntology":null},{"url":null,"slug":"computing-egomotion-with-local-loop-closures","title":"Computing Egomotion with Local Loop Closures for Egocentric Videos","date":"2017-01-17","arxiv_id":"1701.04743","repositories_listed":0,"syntology":null},{"url":null,"slug":"light-field-super-resolution-via-graph-based","title":"Light Field Super-Resolution Via Graph-Based Regularization","date":"2017-01-09","arxiv_id":"1701.02141","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-view-and-multi-view-depth-fusion","title":"Single-View and Multi-View Depth Fusion","date":"2016-11-22","arxiv_id":"1611.07245","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-light-field-imaging-for-improved","title":"Hybrid Light Field Imaging for Improved Spatial Resolution and Depth Range","date":"2016-11-15","arxiv_id":"1611.05008","repositories_listed":0,"syntology":null},{"url":null,"slug":"light-field-stitching-for-extended-synthetic","title":"Light Field Stitching for Extended Synthetic Aperture","date":"2016-11-15","arxiv_id":"1611.05003","repositories_listed":0,"syntology":null}],"record_sha256":"069a4ca9df36a248801c373943292c15d56f76ba2d42fdc9d1c93c67358bda7c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}