{"list":"other","total":129,"rows":[{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2304.02643","title":"Segment Anything","created":"2023-04-05","cited_by_count":8234},{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"id":"1703.06870","title":"Mask R-CNN","created":"2017-03-20","cited_by_count":7277},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"id":"2308.04079","title":"3D Gaussian Splatting for Real-Time Radiance Field Rendering","created":"2023-08-08","cited_by_count":6226},{"id":"2201.05989","title":"Instant Neural Graphics Primitives with a Multiresolution Hash Encoding","created":"2022-01-16","cited_by_count":3723},{"id":"1702.04405","title":"ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes","created":"2017-02-14","cited_by_count":3117},{"id":"1901.05103","title":"DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation","created":"2019-01-16","cited_by_count":2689},{"id":"2112.01527","title":"Masked-attention Mask Transformer for Universal Image Segmentation","created":"2021-12-02","cited_by_count":2351},{"id":"1706.05587","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","created":"2017-06-17","cited_by_count":2161},{"id":"1812.03828","title":"Occupancy Networks: Learning 3D Reconstruction in Function Space","created":"2018-12-10","cited_by_count":2041},{"id":"2103.13413","title":"Vision Transformers for Dense Prediction","created":"2021-03-24","cited_by_count":1803},{"id":"2106.10689","title":"NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction","created":"2021-06-20","cited_by_count":1712},{"id":"2112.05131","title":"Plenoxels: Radiance Fields without Neural Networks","created":"2021-12-09","cited_by_count":1613},{"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":"1712.07629","title":"SuperPoint: Self-Supervised Interest Point Detection and Description","created":"2017-12-20","cited_by_count":1320},{"id":"2107.06278","title":"Per-Pixel Classification is Not All You Need for Semantic Segmentation","created":"2021-07-13","cited_by_count":1152},{"id":"1812.02822","title":"Learning Implicit Fields for Generative Shape Modeling","created":"2018-12-06","cited_by_count":1060},{"id":"1502.00956","title":"ORB-SLAM: a Versatile and Accurate Monocular SLAM System","created":"2015-02-03","cited_by_count":988},{"id":"2104.00680","title":"LoFTR: Detector-Free Local Feature Matching with Transformers","created":"2021-04-01","cited_by_count":984},{"id":"1511.07247","title":"NetVLAD: CNN architecture for weakly supervised place recognition","created":"2015-11-23","cited_by_count":941},{"id":"1906.05797","title":"The Replica Dataset: A Digital Replica of Indoor Spaces","created":"2019-06-13","cited_by_count":904},{"id":"2007.11898","title":"ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM","created":"2020-07-23","cited_by_count":878},{"id":"1610.06475","title":"ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras","created":"2016-10-20","cited_by_count":806},{"id":"2308.11417","title":"ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes","created":"2023-08-22","cited_by_count":764},{"id":"2003.04618","title":"Convolutional Occupancy Networks","created":"2020-03-10","cited_by_count":761},{"id":"2107.02791","title":"Depth-supervised NeRF: Fewer Views and Faster Training for Free","created":"2021-07-06","cited_by_count":735},{"id":"2002.10099","title":"Implicit Geometric Regularization for Learning Shapes","created":"2020-02-24","cited_by_count":710},{"id":"2108.10869","title":"DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras","created":"2021-08-24","cited_by_count":687},{"id":"2302.12288","title":"ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth","created":"2023-02-23","cited_by_count":657},{"id":"2104.06405","title":"BARF: Bundle-Adjusting Neural Radiance Fields","created":"2021-04-13","cited_by_count":642},{"id":"2311.12775","title":"SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering","created":"2023-11-21","cited_by_count":616},{"id":"2112.00724","title":"RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs","created":"2021-12-01","cited_by_count":615},{"id":"2302.04264","title":"Nerfstudio: A Modular Framework for Neural Radiance Field Development","created":"2023-02-08","cited_by_count":600},{"id":"2306.13643","title":"LightGlue: Local Feature Matching at Light Speed","created":"2023-06-23","cited_by_count":590},{"id":"2112.12130","title":"NICE-SLAM: Neural Implicit Scalable Encoding for SLAM","created":"2021-12-22","cited_by_count":589},{"id":"2301.09632","title":"HexPlane: A Fast Representation for Dynamic Scenes","created":"2023-01-23","cited_by_count":587},{"id":"1812.03506","title":"From Coarse to Fine: Robust Hierarchical Localization at Large Scale","created":"2018-12-09","cited_by_count":559},{"id":"2111.11426","title":"Neural Fields in Visual Computing and Beyond","created":"2021-11-22","cited_by_count":540},{"id":"2201.08845","title":"Point-NeRF: Point-based Neural Radiance Fields","created":"2022-01-21","cited_by_count":503},{"id":"2103.12352","title":"iMAP: Implicit Mapping and Positioning in Real-Time","created":"2021-03-23","cited_by_count":476},{"id":"2306.03092","title":"Neuralangelo: High-Fidelity Neural Surface Reconstruction","created":"2023-06-05","cited_by_count":467},{"id":"2312.06741","title":"Gaussian Splatting SLAM","created":"2023-12-11","cited_by_count":415},{"id":"1606.05830","title":"Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age","created":"2016-06-19","cited_by_count":411},{"id":"2312.02126","title":"SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM","created":"2023-12-04","cited_by_count":388},{"id":"2012.05877","title":"INeRF: Inverting Neural Radiance Fields for Pose Estimation","created":"2020-12-10","cited_by_count":382},{"id":"2111.05849","title":"Advances in Neural Rendering","created":"2021-11-10","cited_by_count":365},{"id":"1603.08182","title":"3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions","created":"2016-03-27","cited_by_count":336},{"id":"2110.04994","title":"Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans","created":"2021-10-11","cited_by_count":336},{"id":"2211.06220","title":"OneFormer: One Transformer to Rule Universal Image Segmentation","created":"2022-11-10","cited_by_count":328},{"id":"2111.13680","title":"GMFlow: Learning Optical Flow via Global Matching","created":"2021-11-26","cited_by_count":327},{"id":"2104.04532","title":"Neural RGB-D Surface Reconstruction","created":"2021-04-09","cited_by_count":313},{"id":"2112.03288","title":"Dense Depth Priors for Neural Radiance Fields from Sparse Input Views","created":"2021-12-06","cited_by_count":302},{"id":"2311.11700","title":"GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting","created":"2023-11-20","cited_by_count":278},{"id":"2104.02409","title":"Learning to Estimate Hidden Motions with Global Motion Aggregation","created":"2021-04-06","cited_by_count":224},{"id":"2401.03890","title":"A Survey on 3D Gaussian Splatting","created":"2024-01-08","cited_by_count":224},{"id":"2305.06558","title":"Segment and Track Anything","created":"2023-05-11","cited_by_count":193},{"id":"2208.04726","title":"Deep Patch Visual Odometry","created":"2022-08-08","cited_by_count":190},{"id":"2210.13641","title":"NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields","created":"2022-10-24","cited_by_count":188},{"id":"2304.14377","title":"Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM","created":"2023-04-27","cited_by_count":184},{"id":"1905.02082","title":"ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals","created":"2019-05-06","cited_by_count":181},{"id":"2302.03594","title":"NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM","created":"2023-02-07","cited_by_count":164},{"id":"1709.07492","title":"Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image","created":"2017-09-21","cited_by_count":161},{"id":"2304.04278","title":"Point-SLAM: Dense Neural Point Cloud-based SLAM","created":"2023-04-09","cited_by_count":160},{"id":"2210.00379","title":"NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)","created":"2022-10-01","cited_by_count":158},{"id":"2211.11704","title":"ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields","created":"2022-11-21","cited_by_count":158},{"id":"1808.03856","title":"Neural Importance Sampling","created":"2018-08-11","cited_by_count":155},{"id":"2312.10070","title":"Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting","created":"2023-12-06","cited_by_count":154},{"id":"1704.03489","title":"CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction","created":"2017-04-11","cited_by_count":152},{"id":"1804.00874","title":"CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM","created":"2018-04-03","cited_by_count":152},{"id":"2210.15858","title":"Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation","created":"2022-10-28","cited_by_count":149},{"id":"2311.16728","title":"Photo-SLAM: Real-time Simultaneous Localization and Photorealistic Mapping for Monocular, Stereo, and RGB-D Cameras","created":"2023-11-28","cited_by_count":141},{"id":"2309.02436","title":"GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction","created":"2023-09-05","cited_by_count":133},{"id":"2209.15397","title":"KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way","created":"2022-09-30","cited_by_count":132},{"id":"2402.03246","title":"SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM","created":"2024-02-05","cited_by_count":87},{"id":"2209.13274","title":"Orbeez-SLAM: A Real-time Monocular Visual SLAM with ORB Features and NeRF-realized Mapping","created":"2022-09-27","cited_by_count":79},{"id":"1911.01911","title":"BlenderProc","created":"2019-10-25","cited_by_count":77},{"id":"2302.01838","title":"vMAP: Vectorised Object Mapping for Neural Field SLAM","created":"2023-02-03","cited_by_count":67},{"id":"2207.00254","title":"A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers","created":"2022-07-01","cited_by_count":66},{"id":"2311.11016","title":"SNI-SLAM: Semantic Neural Implicit SLAM","created":"2023-11-18","cited_by_count":65},{"id":"2312.09866","title":"PLGSLAM: Progressive Neural Scene Represenation with Local to Global Bundle Adjustment","created":"2023-12-15","cited_by_count":63},{"id":"2003.05691","title":"The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth","created":"2020-03-12","cited_by_count":61},{"id":"2402.09944","title":"Loopy-SLAM: Dense Neural SLAM with Loop Closures","created":"2024-02-14","cited_by_count":48},{"id":"2102.11870","title":"UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering","created":"2021-02-23","cited_by_count":44},{"id":"2301.08930","title":"Dense RGB SLAM with Neural Implicit Maps","created":"2023-01-21","cited_by_count":42},{"id":"2401.09101","title":"PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency","created":"2024-01-17","cited_by_count":42},{"id":"2303.10709","title":"NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping","created":"2023-03-19","cited_by_count":41},{"id":"2109.12979","title":"CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure","created":"2021-09-27","cited_by_count":38},{"id":"2210.03043","title":"Feature-Realistic Neural Fusion for Real-Time, Open Set Scene Understanding","created":"2022-10-06","cited_by_count":37},{"id":"2006.12567","title":"A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence","created":"2020-06-22","cited_by_count":35},{"id":"2311.08013","title":"CP-SLAM: Collaborative Neural Point-based SLAM System","created":"2023-11-14","cited_by_count":32},{"id":"2310.04787","title":"HI-SLAM: Monocular Real-time Dense Mapping with Hybrid Implicit Fields","created":"2023-10-07","cited_by_count":31},{"id":"2001.02319","title":"Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey","created":"2020-01-08","cited_by_count":30},{"id":"2308.14383","title":"Multi-Modal Neural Radiance Field for Monocular Dense SLAM with a Light-Weight ToF Sensor","created":"2023-08-28","cited_by_count":30},{"id":"2401.14857","title":"LIV-GaussMap: LiDAR-Inertial-Visual Fusion for Real-time 3D Radiance Field Map Rendering","created":"2024-01-26","cited_by_count":28},{"id":"2102.03771","title":"MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square","created":"2021-02-07","cited_by_count":27},{"id":"2306.03000","title":"BeyondPixels: A Comprehensive Review of the Evolution of Neural Radiance Fields","created":"2023-06-05","cited_by_count":27}],"next_cursor":"100"}