{"list":"other","total":56,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1506.01497","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","created":"2015-06-04","cited_by_count":9639},{"id":"1703.06870","title":"Mask R-CNN","created":"2017-03-20","cited_by_count":7277},{"id":"1612.00593","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","created":"2016-12-02","cited_by_count":5248},{"id":"1608.03983","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","created":"2016-08-13","cited_by_count":5045},{"id":"1706.02413","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","created":"2017-06-07","cited_by_count":4401},{"id":"1702.04405","title":"ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes","created":"2017-02-14","cited_by_count":3117},{"id":"1801.07829","title":"Dynamic Graph CNN for Learning on Point Clouds","created":"2018-01-24","cited_by_count":2223},{"id":"1812.05784","title":"PointPillars: Fast Encoders for Object Detection from Point Clouds","created":"2018-12-14","cited_by_count":1715},{"id":"2011.00931","title":"Point Transformer","created":"2020-11-02","cited_by_count":1470},{"id":"1904.08755","title":"4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks","created":"2019-04-18","cited_by_count":1208},{"id":"1904.08889","title":"KPConv: Flexible and Deformable Convolution for Point Clouds","created":"2019-04-18","cited_by_count":1205},{"id":"1912.13192","title":"PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection","created":"2019-12-31","cited_by_count":891},{"id":"1801.00868","title":"Panoptic Segmentation","created":"2018-01-03","cited_by_count":802},{"id":"1801.07791","title":"PointCNN: Convolution On $\\mathcal{X}$-Transformed Points","created":"2018-01-23","cited_by_count":764},{"id":"1711.10275","title":"3D Semantic Segmentation with Submanifold Sparse Convolutional Networks","created":"2017-11-28","cited_by_count":748},{"id":"1811.07246","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","created":"2018-11-17","cited_by_count":657},{"id":"1904.09664","title":"Deep Hough Voting for 3D Object Detection in Point Clouds","created":"2019-04-21","cited_by_count":648},{"id":"1901.02446","title":"Panoptic Feature Pyramid Networks","created":"2019-01-08","cited_by_count":645},{"id":"1711.08488","title":"Frustum PointNets for 3D Object Detection from RGB-D Data","created":"2017-11-22","cited_by_count":640},{"id":"1904.07601","title":"Relation-Shape Convolutional Neural Network for Point Cloud Analysis","created":"2019-04-16","cited_by_count":346},{"id":"1611.05009","title":"OctNet: Learning Deep 3D Representations at High Resolutions","created":"2016-11-15","cited_by_count":342},{"id":"2004.01658","title":"PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation","created":"2020-04-03","cited_by_count":321},{"id":"1704.02901","title":"Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs","created":"2017-04-10","cited_by_count":310},{"id":"1903.00241","title":"Mask Scoring R-CNN","created":"2019-03-01","cited_by_count":266},{"id":"1803.11527","title":"SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters","created":"2018-03-30","cited_by_count":261},{"id":"1812.07003","title":"3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans","created":"2018-12-17","cited_by_count":217},{"id":"2203.01509","title":"SoftGroup for 3D Instance Segmentation on Point Clouds","created":"2022-03-03","cited_by_count":200},{"id":"2003.06537","title":"OccuSeg: Occupancy-aware 3D Instance Segmentation","created":"2020-03-14","cited_by_count":177},{"id":"1711.08588","title":"SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation","created":"2017-11-23","cited_by_count":169},{"id":"1906.01140","title":"Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds","created":"2019-06-04","cited_by_count":156},{"id":"1812.03320","title":"GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud","created":"2018-12-08","cited_by_count":146},{"id":"2003.13867","title":"3D-MPA: Multi Proposal Aggregation for 3D Semantic Instance Segmentation","created":"2020-03-30","cited_by_count":141},{"id":"1712.05245","title":"Pointwise Convolutional Neural Networks","created":"2017-12-14","cited_by_count":127},{"id":"2108.02350","title":"Hierarchical Aggregation for 3D Instance Segmentation","created":"2021-08-05","cited_by_count":127},{"id":"1905.01220","title":"Seamless Scene Segmentation","created":"2019-05-03","cited_by_count":112},{"id":"2103.14962","title":"Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation","created":"2021-03-27","cited_by_count":107},{"id":"2112.04702","title":"Fast Point Transformer","created":"2021-12-09","cited_by_count":104},{"id":"1912.02984","title":"Grid-GCN for Fast and Scalable Point Cloud Learning","created":"2019-12-06","cited_by_count":103},{"id":"2108.07478","title":"Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks","created":"2021-08-17","cited_by_count":99},{"id":"1903.01177","title":"PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things","created":"2019-03-04","cited_by_count":98},{"id":"1902.09852","title":"Associatively Segmenting Instances and Semantics in Point Clouds","created":"2019-02-26","cited_by_count":86},{"id":"2011.11964","title":"LiDAR-based Panoptic Segmentation via Dynamic Shifting Network","created":"2020-11-24","cited_by_count":84},{"id":"1906.08650","title":"3D Instance Segmentation via Multi-Task Metric Learning","created":"2019-06-20","cited_by_count":80},{"id":"2011.13328","title":"DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution","created":"2020-11-26","cited_by_count":75},{"id":"2102.12472","title":"4D Panoptic LiDAR Segmentation","created":"2021-02-24","cited_by_count":65},{"id":"1904.00699","title":"JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields","created":"2019-04-01","cited_by_count":62},{"id":"2205.07002","title":"Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap","created":"2022-05-14","cited_by_count":59},{"id":"2202.06028","title":"OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression","created":"2022-02-12","cited_by_count":58},{"id":"2203.09065","title":"STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset","created":"2022-03-17","cited_by_count":53},{"id":"1912.00145","title":"Point Cloud Instance Segmentation using Probabilistic Embeddings","created":"2019-11-30","cited_by_count":52},{"id":"2010.15157","title":"Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds","created":"2020-10-28","cited_by_count":46},{"id":"1912.05905","title":"LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices","created":"2019-12-12","cited_by_count":38},{"id":"2007.09860","title":"Learning Gaussian Instance Segmentation in Point Clouds","created":"2020-07-20","cited_by_count":33},{"id":"1902.04478","title":"MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation","created":"2019-02-12","cited_by_count":25}],"next_cursor":null}