{"url":"/sota/monocular-depth-estimation-on-kitti-eigen","task":{"name":"Monocular Depth Estimation","url":"/task/monocular-depth-estimation","note":null},"dataset":{"name":"KITTI Eigen split","url":"/dataset/kitti"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Monocular Depth Estimation** is the task of estimating the depth value (distance relative to the camera) of each pixel given a single (monocular) RGB image. This challenging task is a key prerequisite for determining scene understanding for applications such as 3D scene reconstruction, autonomous driving, and AR. State-of-the-art methods usually fall into one of two categories: designing a complex network that is powerful enough to directly regress the depth map, or splitting the input into bins or windows to reduce computational complexity.  The most popular benchmarks are the KITTI and NYUv2 datasets. Models are typically evaluated using RMSE or absolute relative error. \r\n\r\n<span class=\"description-source\">Source: [Defocus Deblurring Using Dual-Pixel Data ](https://arxiv.org/abs/2005.00305)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["absolute relative error","RMSE","Sq Rel","RMSE log","Delta < 1.25","Delta < 1.25^2","Delta < 1.25^3","Square relative error (SqRel)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"absolute relative error":"lower","RMSE":"lower","Sq Rel":null,"RMSE log":"lower","Delta < 1.25":null,"Delta < 1.25^2":null,"Delta < 1.25^3":null,"Square relative error (SqRel)":"lower"}},"counts":{"rows":79,"rows_with_code":68,"rows_with_paper_page":79,"rows_dated":79,"rows_using_additional_data":15},"rows":[{"rank_in_archive_order":1,"model":"SPIDepth","metrics":{"Delta < 1.25":"0.99","Delta < 1.25^2":"0.999","Delta < 1.25^3":"1.000","RMSE":"1.394","RMSE log":"0.048","Sq Rel":"0.069","absolute relative error":"0.029"},"uses_additional_data":true,"paper_date":"2024-04-18","paper":"/paper/spidepth-strengthened-pose-information-for","paper_url":"https://arxiv.org/abs/2404.12501v3","paper_title":"SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimation","code":"https://github.com/Lavreniuk/SPIdepth","n_code_links":1,"syntology":{"n_ran":13,"n_unverified":1,"n_samples":14,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"UniK3D (FT, metric)","metrics":{"Delta < 1.25":"0.990","Delta < 1.25^2":"0.998","Delta < 1.25^3":"0.999","RMSE":"1.68","RMSE log":"0.060","absolute relative error":"0.037"},"uses_additional_data":false,"paper_date":"2025-03-20","paper":"/paper/unik3d-universal-camera-monocular-3d","paper_url":"https://arxiv.org/abs/2503.16591v1","paper_title":"UniK3D: Universal Camera Monocular 3D Estimation","code":"https://github.com/lpiccinelli-eth/UniK3D","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"UniDepthV2 (FT, metric)","metrics":{"Delta < 1.25":"0.989","Delta < 1.25^2":"0.998","Delta < 1.25^3":"0.999","RMSE":"1.71","RMSE log":"0.061","absolute relative error":"0.037"},"uses_additional_data":true,"paper_date":"2025-02-27","paper":"/paper/unidepthv2-universal-monocular-metric-depth","paper_url":"https://arxiv.org/abs/2502.20110v1","paper_title":"UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler","code":"https://github.com/lpiccinelli-eth/unidepth","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":4,"model":"Metric3Dv2 (g2, FT, 80m, flip_aug_test)","metrics":{"Delta < 1.25":"0.989","Delta < 1.25^2":"0.998","Delta < 1.25^3":"1.000","RMSE":"1.766","RMSE log":"0.060","absolute relative error":"0.039"},"uses_additional_data":true,"paper_date":"2024-03-22","paper":"/paper/metric3d-v2-a-versatile-monocular-geometric-1","paper_url":"https://arxiv.org/abs/2404.15506v4","paper_title":"Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation","code":"https://github.com/yvanyin/metric3d","n_code_links":1,"syntology":{"n_ran":3,"n_unverified":0,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"LightedDepth (Video Method)","metrics":{"Delta < 1.25":"0.989","Delta < 1.25^2":"0.998","Delta < 1.25^3":"0.999","RMSE":"1.748","RMSE log":"0.059","Sq Rel":"0.107","absolute relative error":"0.041"},"uses_additional_data":false,"paper_date":"2023-01-01","paper":"/paper/lighteddepth-video-depth-estimation-in-light","paper_url":"http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_LightedDepth_Video_Depth_Estimation_in_Light_of_Limited_Inference_View_CVPR_2023_paper.html","paper_title":"LightedDepth: Video Depth Estimation in Light of Limited Inference View Angles","code":"https://github.com/shngjz/lighteddepth","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"FutureDepth","metrics":{"Delta < 1.25":"0.984","Delta < 1.25^2":"0.998","Delta < 1.25^3":"1.000","RMSE":"1.856","RMSE log":"0.066","Sq Rel":"0.117","Square relative error (SqRel)":"0.117","absolute relative error":"0.041"},"uses_additional_data":false,"paper_date":"2024-03-19","paper":"/paper/futuredepth-learning-to-predict-the-future","paper_url":"https://arxiv.org/abs/2403.12953v2","paper_title":"FutureDepth: Learning to Predict the Future Improves Video Depth Estimation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":7,"model":"UniDepth (Zero-shot)","metrics":{"Delta < 1.25":"0.986","Delta < 1.25^2":"0.998","Delta < 1.25^3":"0.999","RMSE":"1.75","RMSE log":"0.064","absolute relative error":"0.042"},"uses_additional_data":true,"paper_date":"2024-03-27","paper":"/paper/unidepth-universal-monocular-metric-depth","paper_url":"https://arxiv.org/abs/2403.18913v1","paper_title":"UniDepth: Universal Monocular Metric Depth Estimation","code":"https://github.com/lpiccinelli-eth/unidepth","n_code_links":3,"syntology":{"n_ran":15,"n_unverified":11,"n_samples":26,"n_pointer_only_licence":25}},{"rank_in_archive_order":8,"model":"SQLdepth (ConvNeXt-L)","metrics":{"Delta < 1.25":"0.983","Delta < 1.25^2":"0.998","Delta < 1.25^3":"0.999","RMSE":"1.698","RMSE log":"0.064","Sq Rel":"0.105","absolute relative error":"0.043"},"uses_additional_data":false,"paper_date":"2023-09-01","paper":"/paper/sqldepth-generalizable-self-supervised-fine","paper_url":"https://arxiv.org/abs/2309.00526v1","paper_title":"SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation","code":"https://github.com/hisfog/SfMNeXt-Impl","n_code_links":1,"syntology":null},{"rank_in_archive_order":9,"model":"AFNet","metrics":{"Delta < 1.25":"0.980","Delta < 1.25^2":"0.997","Delta < 1.25^3":"0.999","RMSE":"1.712","RMSE log":"0.069","Sq Rel":"0.132","absolute relative error":"0.044"},"uses_additional_data":false,"paper_date":"2024-03-12","paper":"/paper/adaptive-fusion-of-single-view-and-multi-view","paper_url":"https://arxiv.org/abs/2403.07535v1","paper_title":"Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving","code":"https://github.com/junda24/afnet","n_code_links":1,"syntology":{"n_ran":7,"n_unverified":1,"n_samples":8,"n_pointer_only_licence":0}},{"rank_in_archive_order":10,"model":"Depth Anything","metrics":{"Delta < 1.25":"0.982","Delta < 1.25^2":"0.998","Delta < 1.25^3":"1.000","RMSE":"1.896","RMSE log":"0.069","Sq Rel":"0.121","absolute relative error":"0.046"},"uses_additional_data":true,"paper_date":"2024-01-19","paper":"/paper/depth-anything-unleashing-the-power-of-large","paper_url":"https://arxiv.org/abs/2401.10891v2","paper_title":"Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data","code":"https://github.com/LiheYoung/Depth-Anything","n_code_links":7,"syntology":{"n_ran":3,"n_unverified":8,"n_samples":11,"n_pointer_only_licence":0}},{"rank_in_archive_order":11,"model":"MetaPrompt-SD","metrics":{"Delta < 1.25":"0.981","Delta < 1.25^2":"0.998","Delta < 1.25^3":"1.000","RMSE":"1.928","RMSE log":"0.071","Sq Rel":"0.125","absolute relative error":"0.047"},"uses_additional_data":true,"paper_date":"2023-12-22","paper":"/paper/harnessing-diffusion-models-for-visual","paper_url":"https://arxiv.org/abs/2312.14733v1","paper_title":"Harnessing Diffusion Models for Visual Perception with Meta Prompts","code":"https://github.com/fudan-zvg/meta-prompts","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":2,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":12,"model":"ECoDepth","metrics":{"Delta < 1.25":"0.979","Delta < 1.25^2":"0.998","Delta < 1.25^3":"1.000","RMSE":"1.966","RMSE log":"0.074","Sq Rel":"0.139","absolute relative error":"0.048"},"uses_additional_data":false,"paper_date":"2024-03-27","paper":"/paper/ecodepth-effective-conditioning-of-diffusion","paper_url":"https://arxiv.org/abs/2403.18807v4","paper_title":"ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation","code":"https://github.com/aradhye2002/ecodepth","n_code_links":1,"syntology":{"n_ran":11,"n_unverified":4,"n_samples":15,"n_pointer_only_licence":15}},{"rank_in_archive_order":13,"model":"ScaleDepth-K","metrics":{"Delta < 1.25":"0.98","Delta < 1.25^2":"0.998","Delta < 1.25^3":"1.000","RMSE":"1.987","RMSE log":"0.073","Sq Rel":"0.136","absolute relative error":"0.048"},"uses_additional_data":false,"paper_date":"2024-07-11","paper":"/paper/scaledepth-decomposing-metric-depth","paper_url":"https://arxiv.org/abs/2407.08187v1","paper_title":"ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth Estimation","code":"https://github.com/RuijieZhu94/mmdepth/blob/main/projects/ScaleDepth/README.md","n_code_links":1,"syntology":null},{"rank_in_archive_order":14,"model":"EVP","metrics":{"Delta < 1.25":"0.980","Delta < 1.25^2":"0.998","Delta < 1.25^3":"1.000","RMSE":"2.015","RMSE log":"0.073","Sq Rel":"0.136","absolute relative error":"0.048"},"uses_additional_data":false,"paper_date":"2023-12-13","paper":"/paper/evp-enhanced-visual-perception-using-inverse","paper_url":"https://arxiv.org/abs/2312.08548v1","paper_title":"EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment","code":"https://github.com/lavreniuk/evp","n_code_links":1,"syntology":null},{"rank_in_archive_order":15,"model":"GEDepth","metrics":{"Delta < 1.25":"0.9763","Delta < 1.25^2":"0.9972","Delta < 1.25^3":"0.9993","RMSE":"2.044","RMSE log":"0.076","Sq Rel":"0.142","absolute relative error":"0.048"},"uses_additional_data":false,"paper_date":"2023-09-18","paper":"/paper/gedepth-ground-embedding-for-monocular-depth","paper_url":"https://arxiv.org/abs/2309.09975v1","paper_title":"GEDepth: Ground Embedding for Monocular Depth Estimation","code":"https://github.com/qcraftai/gedepth","n_code_links":2,"syntology":null},{"rank_in_archive_order":16,"model":"MAMo","metrics":{"Delta < 1.25":"0.977","Delta < 1.25^2":"0.998","Delta < 1.25^3":"0.9995","RMSE":"1.984","RMSE log":"0.072","Sq Rel":"0.13","absolute relative error":"0.049"},"uses_additional_data":false,"paper_date":"2023-07-26","paper":"/paper/mamo-leveraging-memory-and-attention-for","paper_url":"https://arxiv.org/abs/2307.14336v3","paper_title":"MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":17,"model":"SwinV2-L 1K-MIM","metrics":{"Delta < 1.25":"0.977","Delta < 1.25^2":"0.998","Delta < 1.25^3":"1.000","RMSE":"1.966","RMSE log":"0.075","Sq Rel":"0.139","absolute relative error":"0.050"},"uses_additional_data":false,"paper_date":"2022-05-26","paper":"/paper/revealing-the-dark-secrets-of-masked-image","paper_url":"https://arxiv.org/abs/2205.13543v2","paper_title":"Revealing the Dark Secrets of Masked Image Modeling","code":"https://github.com/SwinTransformer/MIM-Depth-Estimation","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":1,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":18,"model":"IEBins","metrics":{"Delta < 1.25":"0.978","Delta < 1.25^2":"0.998","Delta < 1.25^3":"0.999","RMSE":"2.011","RMSE log":"0.075","Sq Rel":"0.142","absolute relative error":"0.050"},"uses_additional_data":false,"paper_date":"2023-09-25","paper":"/paper/iebins-iterative-elastic-bins-for-monocular-1","paper_url":"https://arxiv.org/abs/2309.14137v1","paper_title":"IEBins: Iterative Elastic Bins for Monocular Depth Estimation","code":"https://github.com/shuweishao/iebins","n_code_links":1,"syntology":{"n_ran":8,"n_unverified":5,"n_samples":13,"n_pointer_only_licence":0}},{"rank_in_archive_order":19,"model":"NDDepth","metrics":{"Delta < 1.25":"0.978","Delta < 1.25^2":"0.998","Delta < 1.25^3":"0.999","RMSE":"2.025","RMSE log":"0.075","Sq Rel":"0.141","absolute relative error":"0.050"},"uses_additional_data":false,"paper_date":"2023-09-19","paper":"/paper/nddepth-normal-distance-assisted-monocular","paper_url":"https://arxiv.org/abs/2309.10592v2","paper_title":"NDDepth: Normal-Distance Assisted Monocular Depth Estimation","code":"https://github.com/ShuweiShao/NDDepth","n_code_links":1,"syntology":null},{"rank_in_archive_order":20,"model":"URCDC-Depth","metrics":{"Delta < 1.25":"0.977","Delta < 1.25^2":"0.997","Delta < 1.25^3":"0.999","RMSE":"2.032","RMSE log":"0.076","Sq Rel":"0.142","absolute relative error":"0.050"},"uses_additional_data":false,"paper_date":"2023-02-16","paper":"/paper/urcdc-depth-uncertainty-rectified-cross","paper_url":"https://arxiv.org/abs/2302.08149v2","paper_title":"URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation","code":"https://github.com/shuweishao/urcdc-depth","n_code_links":1,"syntology":{"n_ran":5,"n_unverified":6,"n_samples":11,"n_pointer_only_licence":0}},{"rank_in_archive_order":21,"model":"iDisc","metrics":{"Delta < 1.25":"0.977","Delta < 1.25^2":"0.997","Delta < 1.25^3":"0.999","RMSE":"2.067","RMSE log":"0.077","Sq Rel":"0.145","absolute relative error":"0.050"},"uses_additional_data":false,"paper_date":"2023-04-13","paper":"/paper/idisc-internal-discretization-for-monocular","paper_url":"https://arxiv.org/abs/2304.06334v1","paper_title":"iDisc: Internal Discretization for Monocular Depth Estimation","code":"https://github.com/lpiccinelli-eth/unidepth","n_code_links":2,"syntology":{"n_ran":5,"n_unverified":1,"n_samples":6,"n_pointer_only_licence":6}},{"rank_in_archive_order":22,"model":"DDP (Swin-L, step-3)","metrics":{"Delta < 1.25":"0.975","Delta < 1.25^2":"0.997","Delta < 1.25^3":"0.999","RMSE":"2.072","RMSE log":"0.076","Sq Rel":"0.148","absolute relative error":"0.050"},"uses_additional_data":false,"paper_date":"2023-03-30","paper":"/paper/ddp-diffusion-model-for-dense-visual","paper_url":"https://arxiv.org/abs/2303.17559v2","paper_title":"DDP: Diffusion Model for Dense Visual Prediction","code":"https://github.com/jiyuanfeng/ddp","n_code_links":1,"syntology":null},{"rank_in_archive_order":23,"model":"MIM-Swin-V2","metrics":{"Delta < 1.25":"0.9757","Delta < 1.25^2":"0.9974","Delta < 1.25^3":"0.9994","RMSE":"2.0373","RMSE log":"0.077","Sq Rel":"0.1458","absolute relative error":"0.0508"},"uses_additional_data":false,"paper_date":"2023-11-07","paper":"/paper/analysis-of-nan-divergence-in-training","paper_url":"https://arxiv.org/abs/2311.03938v1","paper_title":"Analysis of NaN Divergence in Training Monocular Depth Estimation Model","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":24,"model":"PixelFormer","metrics":{"Delta < 1.25":"0.976","Delta < 1.25^2":"0.997","Delta < 1.25^3":"0.999","RMSE":"2.081","RMSE log":"0.077","Sq Rel":"0.149","absolute relative error":"0.051"},"uses_additional_data":true,"paper_date":"2022-10-17","paper":"/paper/attention-attention-everywhere-monocular","paper_url":"https://arxiv.org/abs/2210.09071v1","paper_title":"Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention","code":"https://github.com/ashutosh1807/pixelformer","n_code_links":1,"syntology":{"n_ran":5,"n_unverified":6,"n_samples":11,"n_pointer_only_licence":11}},{"rank_in_archive_order":25,"model":"SwinV2-B 1K-MIM","metrics":{"Delta < 1.25":"0.976","Delta < 1.25^2":"0.998","Delta < 1.25^3":"0.999","RMSE":"2.050","RMSE log":"0.078","Sq Rel":"0.148","absolute relative error":"0.052"},"uses_additional_data":false,"paper_date":"2022-05-26","paper":"/paper/revealing-the-dark-secrets-of-masked-image","paper_url":"https://arxiv.org/abs/2205.13543v2","paper_title":"Revealing the Dark Secrets of Masked Image Modeling","code":"https://github.com/SwinTransformer/MIM-Depth-Estimation","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":1,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":26,"model":"BinsFormer","metrics":{"Delta < 1.25":"0.974","Delta < 1.25^2":"0.997","Delta < 1.25^3":"0.999","RMSE":"2.098","RMSE log":"0.079","Sq Rel":"0.151","absolute relative error":"0.052"},"uses_additional_data":true,"paper_date":"2022-04-03","paper":"/paper/binsformer-revisiting-adaptive-bins-for","paper_url":"https://arxiv.org/abs/2204.00987v1","paper_title":"BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation","code":"https://github.com/zhyever/monocular-depth-estimation-toolbox","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":4,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":27,"model":"NeWCRFs","metrics":{"Delta < 1.25":"0.974","Delta < 1.25^2":"0.997","Delta < 1.25^3":"0.999","RMSE":"2.129","RMSE log":"0.079","Sq Rel":"0.155","absolute relative error":"0.052"},"uses_additional_data":true,"paper_date":"2022-03-03","paper":"/paper/new-crfs-neural-window-fully-connected-crfs-1","paper_url":"https://arxiv.org/abs/2203.01502v2","paper_title":"NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation","code":"https://github.com/aliyun/NeWCRFs","n_code_links":1,"syntology":{"n_ran":5,"n_unverified":5,"n_samples":10,"n_pointer_only_licence":10}},{"rank_in_archive_order":28,"model":"DepthFormer","metrics":{"Delta < 1.25":"0.975","Delta < 1.25^2":"0.997","Delta < 1.25^3":"0.999","RMSE":"2.143","RMSE log":"0.079","Sq Rel":"0.158","absolute relative error":"0.052"},"uses_additional_data":true,"paper_date":"2022-03-27","paper":"/paper/depthformer-exploiting-long-range-correlation","paper_url":"https://arxiv.org/abs/2203.14211v1","paper_title":"DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation","code":"https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox/tree/main/configs/depthformer","n_code_links":1,"syntology":null},{"rank_in_archive_order":29,"model":"MonoDELSNet","metrics":{"Delta < 1.25":"0.969","Delta < 1.25^2":"0.996","Delta < 1.25^3":"0.999","RMSE":"2.101","RMSE log":"0.082","Sq Rel":"0.161","absolute relative error":"0.053"},"uses_additional_data":false,"paper_date":"2021-03-22","paper":"/paper/monocular-depth-estimation-through-virtual","paper_url":"https://arxiv.org/abs/2103.12209v3","paper_title":"Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision","code":"https://github.com/HMRC-AEL/MonoDEVSNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":30,"model":"SfM-Revisited","metrics":{"RMSE":"2.273","Sq Rel":"0.224","absolute relative error":"0.055"},"uses_additional_data":false,"paper_date":"2021-04-01","paper":"/paper/deep-two-view-structure-from-motion-revisited","paper_url":"https://arxiv.org/abs/2104.00556v1","paper_title":"Deep Two-View Structure-from-Motion Revisited","code":"https://github.com/jytime/Deep-SfM-Revisited","n_code_links":1,"syntology":{"n_ran":5,"n_unverified":16,"n_samples":21,"n_pointer_only_licence":0}},{"rank_in_archive_order":31,"model":"D-Net","metrics":{"Delta < 1.25":"0.963","Delta < 1.25^2":"0.995","Delta < 1.25^3":"0.999","RMSE":"2.362","RMSE log":"0.087","Sq Rel":"0.189","absolute relative error":"0.056"},"uses_additional_data":false,"paper_date":"2021-09-29","paper":"/paper/d-net-a-generalised-and-optimised-deep","paper_url":"https://ieeexplore.ieee.org/document/9551940","paper_title":"D-Net: A Generalised and Optimised Deep Network for Monocular Depth Estimation","code":"https://github.com/Joshuat38/D-Net","n_code_links":1,"syntology":null},{"rank_in_archive_order":32,"model":"GLPDepth","metrics":{"Delta < 1.25":"0.967","Delta < 1.25^2":"0.996","Delta < 1.25^3":"0.999","RMSE":"2.297","RMSE log":"0.086","absolute relative error":"0.057"},"uses_additional_data":false,"paper_date":"2022-01-19","paper":"/paper/global-local-path-networks-for-monocular","paper_url":"https://arxiv.org/abs/2201.07436v3","paper_title":"Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth","code":"https://github.com/huggingface/transformers","n_code_links":4,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":0}},{"rank_in_archive_order":33,"model":"NVS-MonoDepth","metrics":{"RMSE":"2.702","absolute relative error":"0.057"},"uses_additional_data":false,"paper_date":"2021-12-22","paper":"/paper/nvs-monodepth-improving-monocular-depth","paper_url":"https://arxiv.org/abs/2112.12577v1","paper_title":"NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":34,"model":"Depthformer","metrics":{"Delta < 1.25":"0.967","Delta < 1.25^2":"0.996","Delta < 1.25^3":"0.999","RMSE":"2.285","RMSE log":" 0.087","Sq Rel":"0.187","absolute relative error":"0.058"},"uses_additional_data":true,"paper_date":"2022-07-10","paper":"/paper/depthformer-multiscale-vision-transformer-for","paper_url":"https://arxiv.org/abs/2207.04535v2","paper_title":"Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information Fusion","code":"https://github.com/ashutosh1807/depthformer","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":35,"model":"AdaBins","metrics":{"Delta < 1.25":"0.964","Delta < 1.25^2":"0.995","Delta < 1.25^3":"0.999","RMSE":"2.360","RMSE log":"0.088","absolute relative error":"0.058"},"uses_additional_data":true,"paper_date":"2020-11-28","paper":"/paper/adabins-depth-estimation-using-adaptive-bins","paper_url":"https://arxiv.org/abs/2011.14141v1","paper_title":"AdaBins: Depth Estimation using Adaptive Bins","code":"https://github.com/shariqfarooq123/AdaBins","n_code_links":11,"syntology":null},{"rank_in_archive_order":36,"model":"Metric3D (zero-shot)","metrics":{"Delta < 1.25":"0.967","Delta < 1.25^2":"0.995","Delta < 1.25^3":"0.999","RMSE":"2.77","absolute relative error":"0.058"},"uses_additional_data":false,"paper_date":"2023-07-20","paper":"/paper/metric3d-towards-zero-shot-metric-3d","paper_url":"https://arxiv.org/abs/2307.10984v1","paper_title":"Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image","code":"https://github.com/yvanyin/metric3d","n_code_links":1,"syntology":null},{"rank_in_archive_order":37,"model":"LapDepth","metrics":{"Delta < 1.25":"0.962","Delta < 1.25^2":"0.994","Delta < 1.25^3":"0.999","RMSE":"2.446","RMSE log":"0.091","absolute relative error":"0.059"},"uses_additional_data":false,"paper_date":"2021-01-08","paper":"/paper/monocular-depth-estimation-using-laplacian","paper_url":"https://ieeexplore.ieee.org/document/9316778","paper_title":"Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals","code":"https://github.com/tjqansthd/LapDepth-release","n_code_links":1,"syntology":null},{"rank_in_archive_order":38,"model":"DPT-Hybrid","metrics":{"Delta < 1.25":"0.959","Delta < 1.25^2":"0.995","Delta < 1.25^3":"0.999","RMSE":"2.573","RMSE log":"0.092","absolute relative error":"0.062"},"uses_additional_data":false,"paper_date":"2021-03-24","paper":"/paper/vision-transformers-for-dense-prediction","paper_url":"https://arxiv.org/abs/2103.13413v1","paper_title":"Vision Transformers for Dense Prediction","code":"https://github.com/huggingface/transformers","n_code_links":15,"syntology":{"n_ran":54,"n_unverified":62,"n_samples":116,"n_pointer_only_licence":15}},{"rank_in_archive_order":39,"model":"BTS","metrics":{"absolute relative error":"0.064"},"uses_additional_data":false,"paper_date":"2019-07-24","paper":"/paper/from-big-to-small-multi-scale-local-planar","paper_url":"https://arxiv.org/abs/1907.10326v6","paper_title":"From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation","code":"https://github.com/cleinc/bts","n_code_links":14,"syntology":{"n_ran":5,"n_unverified":10,"n_samples":15,"n_pointer_only_licence":4}},{"rank_in_archive_order":40,"model":"DINOv2 (ViT-g/14 frozen, w/ DPT decoder)","metrics":{"Delta < 1.25":"0.968","Delta < 1.25^2":"0.997","Delta < 1.25^3":"0.9993","RMSE":"2.1128","RMSE log":"0.0882","Sq Rel":"0.1797","absolute relative error":"0.0652"},"uses_additional_data":false,"paper_date":"2023-04-14","paper":"/paper/dinov2-learning-robust-visual-features","paper_url":"https://arxiv.org/abs/2304.07193v2","paper_title":"DINOv2: Learning Robust Visual Features without Supervision","code":"https://github.com/huggingface/transformers","n_code_links":26,"syntology":{"n_ran":21,"n_unverified":25,"n_samples":46,"n_pointer_only_licence":12}},{"rank_in_archive_order":41,"model":"LightDepth","metrics":{"RMSE":"2.923","absolute relative error":"0.070"},"uses_additional_data":false,"paper_date":"2022-11-16","paper":"/paper/lightdepth-a-resource-efficient-depth","paper_url":"https://arxiv.org/abs/2211.08608v2","paper_title":"LightDepth: A Resource Efficient Depth Estimation Approach for Dealing with Ground Truth Sparsity via Curriculum Learning","code":"https://github.com/fatemehkarimii/lightdepth","n_code_links":1,"syntology":null},{"rank_in_archive_order":42,"model":"DORN","metrics":{"Delta < 1.25":"0.932","Delta < 1.25^2":"0.984","Delta < 1.25^3":"0.994","RMSE":"2.727","RMSE log":"0.120","absolute relative error":"0.072"},"uses_additional_data":false,"paper_date":"2018-06-06","paper":"/paper/deep-ordinal-regression-network-for-monocular","paper_url":"http://arxiv.org/abs/1806.02446v1","paper_title":"Deep Ordinal Regression Network for Monocular Depth Estimation","code":"https://github.com/hufu6371/DORN","n_code_links":5,"syntology":{"n_ran":0,"n_unverified":3,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":43,"model":"VNL","metrics":{"absolute relative error":"0.072"},"uses_additional_data":false,"paper_date":"2019-07-29","paper":"/paper/enforcing-geometric-constraints-of-virtual","paper_url":"https://arxiv.org/abs/1907.12209v2","paper_title":"Enforcing geometric constraints of virtual normal for depth prediction","code":"https://github.com/aim-uofa/AdelaiDepth","n_code_links":3,"syntology":null},{"rank_in_archive_order":44,"model":"PrimeDepth + Depth Anything","metrics":{"Delta < 1.25":"0.953","absolute relative error":"0.073"},"uses_additional_data":false,"paper_date":"2024-09-13","paper":"/paper/primedepth-efficient-monocular-depth","paper_url":"https://arxiv.org/abs/2409.09144v1","paper_title":"PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage","code":"https://github.com/vislearn/PrimeDepth","n_code_links":1,"syntology":{"n_ran":13,"n_unverified":2,"n_samples":15,"n_pointer_only_licence":0}},{"rank_in_archive_order":45,"model":"DSN","metrics":{"absolute relative error":"0.075"},"uses_additional_data":false,"paper_date":"2020-10-13","paper":"/paper/on-deep-learning-techniques-to-boost","paper_url":"https://arxiv.org/abs/2010.06626v2","paper_title":"On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":46,"model":"PrimeDepth","metrics":{"Delta < 1.25":"0.937","absolute relative error":"0.079"},"uses_additional_data":false,"paper_date":"2024-09-13","paper":"/paper/primedepth-efficient-monocular-depth","paper_url":"https://arxiv.org/abs/2409.09144v1","paper_title":"PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage","code":"https://github.com/vislearn/PrimeDepth","n_code_links":1,"syntology":{"n_ran":13,"n_unverified":2,"n_samples":15,"n_pointer_only_licence":0}},{"rank_in_archive_order":47,"model":"Focal-WNet","metrics":{"Delta < 1.25":"0.926","Delta < 1.25^2":"0.986","Delta < 1.25^3":"0.997","RMSE":"3.076","RMSE log":"0.120","absolute relative error":"0.082"},"uses_additional_data":false,"paper_date":"2022-07-18","paper":"/paper/focal-wnet-an-architecture-unifying","paper_url":"https://ieeexplore.ieee.org/abstract/document/9824488","paper_title":"Focal-WNet: An Architecture Unifying Convolution and Attention for Depth Estimation","code":"https://github.com/Goubeast/Focal-WNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":48,"model":"DepthMaster","metrics":{"Delta < 1.25":"0.937","absolute relative error":"0.082"},"uses_additional_data":true,"paper_date":"2025-01-05","paper":"/paper/depthmaster-taming-diffusion-models-for","paper_url":"https://arxiv.org/abs/2501.02576v1","paper_title":"DepthMaster: Taming Diffusion Models for Monocular Depth Estimation","code":"https://github.com/indu1ge/DepthMaster","n_code_links":1,"syntology":null},{"rank_in_archive_order":49,"model":"Manydepth2","metrics":{"Delta < 1.25":"0.909","Delta < 1.25^2":"0.968","Delta < 1.25^3":"0.984","RMSE":"4.232","RMSE log":"0.649","Sq Rel":"0.170","absolute relative error":"0.091"},"uses_additional_data":false,"paper_date":"2023-12-23","paper":"/paper/mgdepth-motion-guided-cost-volume-for-self","paper_url":"https://arxiv.org/abs/2312.15268v8","paper_title":"Manydepth2: Motion-Aware Self-Supervised Multi-Frame Monocular Depth Estimation in Dynamic Scenes","code":"https://github.com/kaichen-z/rad","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":9,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":50,"model":"VOMonodepth","metrics":{"absolute relative error":"0.091"},"uses_additional_data":false,"paper_date":"2019-08-08","paper":"/paper/enhancing-self-supervised-monocular-depth","paper_url":"https://arxiv.org/abs/1908.03127v2","paper_title":"Enhancing self-supervised monocular depth estimation with traditional visual odometry","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":51,"model":"DenseDepth","metrics":{"absolute relative error":"0.093"},"uses_additional_data":false,"paper_date":"2018-12-31","paper":"/paper/high-quality-monocular-depth-estimation-via","paper_url":"http://arxiv.org/abs/1812.11941v2","paper_title":"High Quality Monocular Depth Estimation via Transfer Learning","code":"https://github.com/ialhashim/DenseDepth","n_code_links":45,"syntology":{"n_ran":5,"n_unverified":18,"n_samples":23,"n_pointer_only_licence":3}},{"rank_in_archive_order":52,"model":"SVS","metrics":{"absolute relative error":"0.094"},"uses_additional_data":false,"paper_date":"2018-03-07","paper":"/paper/single-view-stereo-matching","paper_url":"http://arxiv.org/abs/1803.02612v2","paper_title":"Single View Stereo Matching","code":"https://github.com/lawy623/SVS","n_code_links":1,"syntology":null},{"rank_in_archive_order":53,"model":"monoResMatch","metrics":{"absolute relative error":"0.096"},"uses_additional_data":false,"paper_date":"2019-04-08","paper":"/paper/learning-monocular-depth-estimation-infusing","paper_url":"http://arxiv.org/abs/1904.04144v1","paper_title":"Learning monocular depth estimation infusing traditional stereo knowledge","code":"https://github.com/fabiotosi92/monoResMatch-Tensorflow","n_code_links":1,"syntology":null},{"rank_in_archive_order":54,"model":"SemiDepth","metrics":{"absolute relative error":"0.096"},"uses_additional_data":false,"paper_date":"2019-05-18","paper":"/paper/semi-supervised-monocular-depth-estimation","paper_url":"https://arxiv.org/abs/1905.07542v1","paper_title":"Semi-Supervised Monocular Depth Estimation with Left-Right Consistency Using Deep Neural Network","code":"https://github.com/jahaniam/semidepth","n_code_links":2,"syntology":null},{"rank_in_archive_order":55,"model":"CFA","metrics":{"absolute relative error":"0.096"},"uses_additional_data":false,"paper_date":"2018-03-21","paper":"/paper/monocular-depth-estimation-by-learning-from","paper_url":"http://arxiv.org/abs/1803.08018v2","paper_title":"Monocular Depth Estimation by Learning from Heterogeneous Datasets","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":56,"model":"Depth Hints","metrics":{"absolute relative error":"0.096"},"uses_additional_data":false,"paper_date":"2019-09-19","paper":"/paper/self-supervised-monocular-depth-hints","paper_url":"https://arxiv.org/abs/1909.09051v1","paper_title":"Self-Supervised Monocular Depth Hints","code":"https://github.com/nianticlabs/depth-hints","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":57,"model":"SOM","metrics":{"absolute relative error":"0.097"},"uses_additional_data":false,"paper_date":"2019-09-10","paper":"/paper/structure-attentioned-memory-network-for","paper_url":"https://arxiv.org/abs/1909.04594v1","paper_title":"Structure-Attentioned Memory Network for Monocular Depth Estimation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":58,"model":"Marigold","metrics":{"Delta < 1.25":"0.916","Delta < 1.25^2":"0.987","Delta < 1.25^3":"0.996","RMSE":"3.304","RMSE log":"0.138","absolute relative error":"0.099"},"uses_additional_data":true,"paper_date":"2023-12-04","paper":"/paper/repurposing-diffusion-based-image-generators","paper_url":"https://arxiv.org/abs/2312.02145v2","paper_title":"Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation","code":"https://github.com/prs-eth/marigold","n_code_links":4,"syntology":{"n_ran":14,"n_unverified":12,"n_samples":26,"n_pointer_only_licence":0}},{"rank_in_archive_order":59,"model":"GCNDepth","metrics":{"Delta < 1.25":"0.888","Delta < 1.25^2":"0.965","Delta < 1.25^3":"0.984","RMSE":"4.494","RMSE log":"0.181","absolute relative error":"0.104"},"uses_additional_data":false,"paper_date":"2021-12-13","paper":"/paper/gcndepth-self-supervised-monocular-depth","paper_url":"https://arxiv.org/abs/2112.06782v1","paper_title":"GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network","code":"https://github.com/arminmasoumian/gcndepth","n_code_links":1,"syntology":null},{"rank_in_archive_order":60,"model":"monodepth2 M","metrics":{"absolute relative error":"0.106"},"uses_additional_data":true,"paper_date":"2018-06-04","paper":"/paper/digging-into-self-supervised-monocular-depth","paper_url":"https://arxiv.org/abs/1806.01260v4","paper_title":"Digging Into Self-Supervised Monocular Depth Estimation","code":"https://github.com/nianticlabs/monodepth2","n_code_links":15,"syntology":{"n_ran":17,"n_unverified":7,"n_samples":24,"n_pointer_only_licence":6}},{"rank_in_archive_order":61,"model":"DeepLabV3+ (F10)","metrics":{"absolute relative error":"0.110"},"uses_additional_data":false,"paper_date":"2020-01-14","paper":"/paper/single-image-depth-estimation-trained-via-1","paper_url":"https://arxiv.org/abs/2001.05036v1","paper_title":"Single Image Depth Estimation Trained via Depth from Defocus Cues","code":"https://github.com/shirgur/UnsupervisedDepthFromFocus","n_code_links":1,"syntology":null},{"rank_in_archive_order":62,"model":"DiPE","metrics":{"absolute relative error":"0.112"},"uses_additional_data":false,"paper_date":"2020-03-03","paper":"/paper/dipe-deeper-into-photometric-errors-for","paper_url":"https://arxiv.org/abs/2003.01360v3","paper_title":"DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular Videos","code":"https://github.com/HalleyJiang/DiPE","n_code_links":1,"syntology":null},{"rank_in_archive_order":63,"model":"DNet","metrics":{"Delta < 1.25":"0.877","Delta < 1.25^2":"0.960","Delta < 1.25^3":"0.981","RMSE":"4.812","RMSE log":"0.191","absolute relative error":"0.113"},"uses_additional_data":false,"paper_date":"2020-04-12","paper":"/paper/toward-hierarchical-self-supervised-monocular","paper_url":"https://arxiv.org/abs/2004.05560v2","paper_title":"Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications","code":"https://github.com/TJ-IPLab/DNet","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":64,"model":"LSIM","metrics":{"absolute relative error":"0.113"},"uses_additional_data":false,"paper_date":"2019-05-01","paper":"/paper/learn-stereo-infer-mono-siamese-networks-for","paper_url":"http://arxiv.org/abs/1905.00401v1","paper_title":"Learn Stereo, Infer Mono: Siamese Networks for Self-Supervised, Monocular, Depth Estimation","code":"https://github.com/mtngld/lsim","n_code_links":1,"syntology":null},{"rank_in_archive_order":65,"model":"SC-Depth (ResNet 50)","metrics":{"Delta < 1.25":"0.873","Delta < 1.25^2":"0.960","Delta < 1.25^3":"0.982","RMSE":"4.706","RMSE log":"0.191","absolute relative error":"0.114"},"uses_additional_data":false,"paper_date":"2021-05-25","paper":"/paper/unsupervised-scale-consistent-depth-learning","paper_url":"https://arxiv.org/abs/2105.11610v1","paper_title":"Unsupervised Scale-consistent Depth Learning from Video","code":"https://github.com/JiawangBian/SC-SfMLearner-Release","n_code_links":2,"syntology":null},{"rank_in_archive_order":66,"model":"SemanticAware","metrics":{"absolute relative error":"0.118"},"uses_additional_data":false,"paper_date":"2019-06-01","paper":"/paper/towards-scene-understanding-unsupervised","paper_url":"http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Towards_Scene_Understanding_Unsupervised_Monocular_Depth_Estimation_With_Semantic-Aware_Representation_CVPR_2019_paper.html","paper_title":"Towards Scene Understanding: Unsupervised Monocular Depth Estimation With Semantic-Aware Representation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":67,"model":"SC-Depth (ResNet18)","metrics":{"Delta < 1.25":"0.863","Delta < 1.25^2":"0.957","Delta < 1.25^3":"0.981","RMSE":"4.950","RMSE log":"0.197","absolute relative error":"0.119"},"uses_additional_data":false,"paper_date":"2021-05-25","paper":"/paper/unsupervised-scale-consistent-depth-learning","paper_url":"https://arxiv.org/abs/2105.11610v1","paper_title":"Unsupervised Scale-consistent Depth Learning from Video","code":"https://github.com/JiawangBian/SC-SfMLearner-Release","n_code_links":2,"syntology":null},{"rank_in_archive_order":68,"model":"PackNet-SfM","metrics":{"absolute relative error":"0.12"},"uses_additional_data":false,"paper_date":"2019-05-06","paper":"/paper/packnet-sfm-3d-packing-for-self-supervised","paper_url":"https://arxiv.org/abs/1905.02693v4","paper_title":"3D Packing for Self-Supervised Monocular Depth Estimation","code":"https://github.com/TRI-ML/packnet-sfm","n_code_links":4,"syntology":null},{"rank_in_archive_order":69,"model":"3Net","metrics":{"absolute relative error":"0.126"},"uses_additional_data":false,"paper_date":"2018-08-05","paper":"/paper/learning-monocular-depth-estimation-with","paper_url":"http://arxiv.org/abs/1808.01606v1","paper_title":"Learning monocular depth estimation with unsupervised trinocular assumptions","code":"https://github.com/mattpoggi/3net","n_code_links":1,"syntology":null},{"rank_in_archive_order":70,"model":"SC-SfMLearner_CS+K","metrics":{"absolute relative error":"0.128"},"uses_additional_data":false,"paper_date":"2019-08-28","paper":"/paper/unsupervised-scale-consistent-depth-and-ego","paper_url":"https://arxiv.org/abs/1908.10553v2","paper_title":"Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video","code":"https://github.com/JiawangBian/SC-SfMLearner-Release","n_code_links":2,"syntology":{"n_ran":2,"n_unverified":1,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":71,"model":"SelfDepthNorm","metrics":{"absolute relative error":"0.133"},"uses_additional_data":false,"paper_date":"2019-03-01","paper":"/paper/self-supervised-learning-for-single-view","paper_url":"http://arxiv.org/abs/1903.00112v1","paper_title":"Self-supervised Learning for Single View Depth and Surface Normal Estimation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":72,"model":"SIGNet","metrics":{"absolute relative error":"0.133"},"uses_additional_data":false,"paper_date":"2018-12-13","paper":"/paper/signet-semantic-instance-aided-unsupervised","paper_url":"http://arxiv.org/abs/1812.05642v2","paper_title":"SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception","code":"https://github.com/mengyuest/SIGNet","n_code_links":2,"syntology":null},{"rank_in_archive_order":73,"model":"struct2depth","metrics":{"absolute relative error":"0.135"},"uses_additional_data":false,"paper_date":"2018-11-15","paper":"/paper/depth-prediction-without-the-sensors","paper_url":"http://arxiv.org/abs/1811.06152v1","paper_title":"Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos","code":"https://github.com/tensorflow/models/tree/master/research/struct2depth","n_code_links":11,"syntology":{"n_ran":1,"n_unverified":2,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":74,"model":"SC-SfMLearner","metrics":{"absolute relative error":"0.137"},"uses_additional_data":false,"paper_date":"2019-08-28","paper":"/paper/unsupervised-scale-consistent-depth-and-ego","paper_url":"https://arxiv.org/abs/1908.10553v2","paper_title":"Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video","code":"https://github.com/JiawangBian/SC-SfMLearner-Release","n_code_links":2,"syntology":{"n_ran":2,"n_unverified":1,"n_samples":3,"n_pointer_only_licence":3}},{"rank_in_archive_order":75,"model":"CC","metrics":{"absolute relative error":"0.140"},"uses_additional_data":false,"paper_date":"2018-05-24","paper":"/paper/competitive-collaboration-joint-unsupervised","paper_url":"http://arxiv.org/abs/1805.09806v3","paper_title":"Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation","code":"https://github.com/anuragranj/cc","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":13,"n_samples":14,"n_pointer_only_licence":0}},{"rank_in_archive_order":76,"model":"SIW","metrics":{"absolute relative error":"0.14"},"uses_additional_data":false,"paper_date":"2022-02-04","paper":"/paper/the-devil-is-in-the-labels-semantic","paper_url":"https://arxiv.org/abs/2202.02002v2","paper_title":"Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":77,"model":"LeReS","metrics":{"Delta < 1.25":"0.784","absolute relative error":"0.149"},"uses_additional_data":false,"paper_date":"2020-12-17","paper":"/paper/learning-to-recover-3d-scene-shape-from-a","paper_url":"https://arxiv.org/abs/2012.09365v1","paper_title":"Learning to Recover 3D Scene Shape from a Single Image","code":"https://github.com/aim-uofa/AdelaiDepth","n_code_links":1,"syntology":null},{"rank_in_archive_order":78,"model":"GASDA","metrics":{"absolute relative error":"0.149"},"uses_additional_data":false,"paper_date":"2019-04-03","paper":"/paper/geometry-aware-symmetric-domain-adaptation","paper_url":"http://arxiv.org/abs/1904.01870v1","paper_title":"Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation","code":"https://github.com/sshan-zhao/GASDA","n_code_links":1,"syntology":null},{"rank_in_archive_order":79,"model":"VDA","metrics":{"absolute relative error":"0.193"},"uses_additional_data":false,"paper_date":"2019-03-26","paper":"/paper/veritatem-dies-aperit-temporally-consistent","paper_url":"https://arxiv.org/abs/1903.10764v2","paper_title":"Veritatem Dies Aperit- Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach","code":"https://github.com/atapour/temporal-depth-segmentation","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":35,"rows_with_any_sample_ran":33,"distinct_papers_with_graph_line":32,"distinct_papers_with_any_sample_ran":30,"samples_over_distinct_papers":{"n_ran":235,"n_unverified":237,"n_samples":472,"n_pointer_only_licence":121,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":254,"n_unverified":241,"n_samples":495,"n_pointer_only_licence":124,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}