{"list":"other","total":93,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2304.02643","title":"Segment Anything","created":"2023-04-05","cited_by_count":8234},{"id":"2012.12877","title":"Training data-efficient image transformers & distillation through attention","created":"2020-12-23","cited_by_count":4586},{"id":"1606.00915","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","created":"2016-06-02","cited_by_count":3241},{"id":"2105.15203","title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","created":"2021-05-31","cited_by_count":2940},{"id":"1912.04838","title":"Scalability in Perception for Autonomous Driving: Waymo Open Dataset","created":"2019-12-10","cited_by_count":2451},{"id":"2003.12039","title":"RAFT: Recurrent All-Pairs Field Transforms for Optical Flow","created":"2020-03-26","cited_by_count":2415},{"id":"2103.13413","title":"Vision Transformers for Dense Prediction","created":"2021-03-24","cited_by_count":1803},{"id":"2111.09881","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","created":"2021-11-18","cited_by_count":1627},{"id":"1907.01341","title":"Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer","created":"2019-07-02","cited_by_count":1495},{"id":"2401.10891","title":"Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data","created":"2024-01-19","cited_by_count":1360},{"id":"1406.2283","title":"Depth Map Prediction from a Single Image using a Multi-Scale Deep Network","created":"2014-06-09","cited_by_count":1274},{"id":"1709.02371","title":"PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume","created":"2017-09-07","cited_by_count":968},{"id":"2309.16588","title":"Vision Transformers Need Registers","created":"2023-09-28","cited_by_count":871},{"id":"1609.03677","title":"Unsupervised Monocular Depth Estimation with Left-Right Consistency","created":"2016-09-13","cited_by_count":820},{"id":"1704.07813","title":"Unsupervised Learning of Depth and Ego-Motion from Video","created":"2017-04-25","cited_by_count":782},{"id":"2308.11417","title":"ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes","created":"2023-08-22","cited_by_count":764},{"id":"1806.02446","title":"Deep Ordinal Regression Network for Monocular Depth Estimation","created":"2018-06-06","cited_by_count":723},{"id":"2011.02523","title":"Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding","created":"2020-11-04","cited_by_count":604},{"id":"2011.14141","title":"AdaBins: Depth Estimation using Adaptive Bins","created":"2020-11-28","cited_by_count":601},{"id":"2109.13410","title":"KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D","created":"2021-09-28","cited_by_count":575},{"id":"1411.4734","title":"Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture","created":"2014-11-18","cited_by_count":483},{"id":"1905.02693","title":"3D Packing for Self-Supervised Monocular Depth Estimation","created":"2019-05-06","cited_by_count":439},{"id":"1912.06378","title":"Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching","created":"2019-12-13","cited_by_count":417},{"id":"2410.02073","title":"Depth Pro: Sharp Monocular Metric Depth in Less Than a Second","created":"2024-10-02","cited_by_count":404},{"id":"1606.00373","title":"Deeper Depth Prediction with Fully Convolutional Residual Networks","created":"2016-06-01","cited_by_count":403},{"id":"2403.18913","title":"UniDepth: Universal Monocular Metric Depth Estimation","created":"2024-03-27","cited_by_count":382},{"id":"1603.04992","title":"Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue","created":"2016-03-16","cited_by_count":368},{"id":"1907.12209","title":"Enforcing geometric constraints of virtual normal for depth prediction","created":"2019-07-29","cited_by_count":234},{"id":"2012.01411","title":"PatchmatchNet: Learned Multi-View Patchmatch Stereo","created":"2020-12-02","cited_by_count":207},{"id":"2004.09548","title":"AANet: Adaptive Aggregation Network for Efficient Stereo Matching","created":"2020-04-20","cited_by_count":202},{"id":"2007.10042","title":"Non-Local Spatial Propagation Network for Depth Completion","created":"2020-07-20","cited_by_count":195},{"id":"2005.06209","title":"On the uncertainty of self-supervised monocular depth estimation","created":"2020-05-13","cited_by_count":182},{"id":"1802.05522","title":"Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints","created":"2018-02-15","cited_by_count":179},{"id":"2502.20110","title":"UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler","created":"2025-02-27","cited_by_count":173},{"id":"1502.07411","title":"Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields","created":"2015-02-26","cited_by_count":169},{"id":"1803.03893","title":"Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction","created":"2018-03-11","cited_by_count":159},{"id":"1712.00175","title":"Learning Depth from Monocular Videos using Direct Methods","created":"2017-12-01","cited_by_count":154},{"id":"2203.01502","title":"NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation","created":"2022-03-03","cited_by_count":153},{"id":"2103.00783","title":"PENet: Towards Precise and Efficient Image Guided Depth Completion","created":"2021-03-01","cited_by_count":146},{"id":"2208.03543","title":"MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer","created":"2022-08-06","cited_by_count":136},{"id":"2002.12319","title":"Semantically-Guided Representation Learning for Self-Supervised Monocular Depth","created":"2020-02-27","cited_by_count":134},{"id":"1909.09051","title":"Self-Supervised Monocular Depth Hints","created":"2019-09-19","cited_by_count":127},{"id":"2203.14211","title":"DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation","created":"2022-03-27","cited_by_count":118},{"id":"2012.07356","title":"HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation","created":"2020-12-14","cited_by_count":115},{"id":"2211.13202","title":"Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation","created":"2022-11-23","cited_by_count":100},{"id":"2401.18084","title":"Binding Touch to Everything: Learning Unified Multimodal Tactile Representations","created":"2024-01-31","cited_by_count":95},{"id":"1905.08616","title":"Unsupervised Depth Completion from Visual Inertial Odometry","created":"2019-05-15","cited_by_count":89},{"id":"2304.14340","title":"SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection","created":"2023-04-27","cited_by_count":83},{"id":"2006.11527","title":"Memory Transformer","created":"2020-06-20","cited_by_count":76},{"id":"2202.09769","title":"Dynamic Spatial Propagation Network for Depth Completion","created":"2022-02-20","cited_by_count":71},{"id":"1704.02157","title":"Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation","created":"2017-04-07","cited_by_count":70},{"id":"1904.04144","title":"Learning monocular depth estimation infusing traditional stereo knowledge","created":"2019-04-08","cited_by_count":64},{"id":"2108.10531","title":"Unsupervised Depth Completion with Calibrated Backprojection Layers","created":"2021-08-24","cited_by_count":61},{"id":"1901.10034","title":"Dense Depth Posterior (DDP) from Single Image and Sparse Range","created":"2019-01-28","cited_by_count":57},{"id":"2008.04017","title":"SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving","created":"2020-08-10","cited_by_count":48},{"id":"2109.12484","title":"Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation","created":"2021-09-26","cited_by_count":48},{"id":"1803.05648","title":"LEGO: Learning Edge with Geometry all at Once by Watching Videos","created":"2018-03-15","cited_by_count":47},{"id":"1903.07309","title":"Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction","created":"2019-03-18","cited_by_count":44},{"id":"2006.08602","title":"Targeted Adversarial Perturbations for Monocular Depth Prediction","created":"2020-06-12","cited_by_count":40},{"id":"2501.02464","title":"Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera","created":"2025-01-05","cited_by_count":40},{"id":"2106.02994","title":"Learning Topology from Synthetic Data for Unsupervised Depth Completion","created":"2021-06-06","cited_by_count":38},{"id":"1808.01606","title":"Learning monocular depth estimation with unsupervised trinocular assumptions","created":"2018-08-05","cited_by_count":36},{"id":"2404.03635","title":"WorDepth: Variational Language Prior for Monocular Depth Estimation","created":"2024-04-04","cited_by_count":34},{"id":"1807.11130","title":"Geo-Supervised Visual Depth Prediction","created":"2018-07-30","cited_by_count":33},{"id":"1804.04784","title":"FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification","created":"2018-04-13","cited_by_count":32},{"id":"2206.10779","title":"Not Just Streaks: Towards Ground Truth for Single Image Deraining","created":"2022-06-22","cited_by_count":31},{"id":"2106.03010","title":"An Adaptive Framework for Learning Unsupervised Depth Completion","created":"2021-06-06","cited_by_count":30},{"id":"2402.03312","title":"Test-Time Adaptation for Depth Completion","created":"2024-02-05","cited_by_count":26},{"id":"2009.12840","title":"Adaptive confidence thresholding for monocular depth estimation","created":"2020-09-27","cited_by_count":25},{"id":"2203.16034","title":"Monitored Distillation for Positive Congruent Depth Completion","created":"2022-03-30","cited_by_count":24},{"id":"2410.02924","title":"RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions","created":"2024-10-03","cited_by_count":21},{"id":"2009.10142","title":"Stereopagnosia: Fooling Stereo Networks with Adversarial Perturbations","created":"2020-09-21","cited_by_count":19},{"id":"2112.06116","title":"Stereoscopic Universal Perturbations across Different Architectures and Datasets","created":"2021-12-12","cited_by_count":17},{"id":"2312.00944","title":"Enhancing Diffusion Models with 3D Perspective Geometry Constraints","created":"2023-12-01","cited_by_count":16},{"id":"2310.09739","title":"AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimation","created":"2023-10-15","cited_by_count":15},{"id":"2312.00836","title":"Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration","created":"2023-12-01","cited_by_count":14},{"id":"2308.09040","title":"SimFIR: A Simple Framework for Fisheye Image Rectification with Self-supervised Representation Learning","created":"2023-08-17","cited_by_count":13},{"id":"2503.12745","title":"ProtoDepth: Unsupervised Continual Depth Completion with Prototypes","created":"2025-03-17","cited_by_count":11},{"id":"2506.05480","title":"ODE-GS: Latent ODEs for Dynamic Scene Extrapolation with 3D Gaussian Splatting","created":"2025-06-05","cited_by_count":11},{"id":"2304.14190","title":"Quadric Representations for LiDAR Odometry, Mapping and Localization","created":"2023-04-27","cited_by_count":9},{"id":"2310.03967","title":"Sub-token ViT Embedding via Stochastic Resonance Transformers","created":"2023-10-06","cited_by_count":9},{"id":"2312.00837","title":"Adaptive Correspondence Scoring for Unsupervised Medical Image Registration","created":"2023-12-01","cited_by_count":9},{"id":"1908.11092","title":"Minimum Delay Object Detection From Video","created":"2019-08-29","cited_by_count":8},{"id":"2401.13786","title":"FoVA-Depth: Field-of-View Agnostic Depth Estimation for Cross-Dataset Generalization","created":"2024-01-24","cited_by_count":8},{"id":"2405.17315","title":"All-day Depth Completion","created":"2024-05-27","cited_by_count":8},{"id":"2409.15054","title":"FisheyeDepth: A Real Scale Self-Supervised Depth Estimation Model for Fisheye Camera","created":"2024-09-23","cited_by_count":8},{"id":"2405.03662","title":"Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation","created":"2024-05-06","cited_by_count":6},{"id":"2109.08791","title":"Small Lesion Segmentation in Brain MRIs with Subpixel Embedding","created":"2021-09-18","cited_by_count":5},{"id":"2410.18074","title":"UnCLe: Benchmarking Unsupervised Continual Learning for Depth Completion","created":"2024-10-23","cited_by_count":5},{"id":"2503.16616","title":"Progressive Test Time Energy Adaptation for Medical Image Segmentation","created":"2025-03-20","cited_by_count":5},{"id":"2503.17182","title":"Radar-Guided Polynomial Fitting for Metric Depth Estimation","created":"2025-03-21","cited_by_count":5}],"next_cursor":null}