{"list":"other","total":67,"rows":[{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1703.06870","title":"Mask R-CNN","created":"2017-03-20","cited_by_count":7277},{"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":"1512.03012","title":"ShapeNet: An Information-Rich 3D Model Repository","created":"2015-12-09","cited_by_count":2897},{"id":"1803.08494","title":"Group Normalization","created":"2018-03-22","cited_by_count":1524},{"id":"1612.00603","title":"A Point Set Generation Network for 3D Object Reconstruction from a Single Image","created":"2016-12-02","cited_by_count":979},{"id":"1711.00199","title":"PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes","created":"2017-11-01","cited_by_count":775},{"id":"1804.01654","title":"Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images","created":"2018-04-05","cited_by_count":651},{"id":"1604.00449","title":"3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction","created":"2016-04-02","cited_by_count":588},{"id":"1802.05384","title":"AtlasNet: A Papier-M\\^ach\\'e Approach to Learning 3D Surface Generation","created":"2018-02-15","cited_by_count":539},{"id":"1901.02970","title":"Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation","created":"2019-01-09","cited_by_count":495},{"id":"2005.09007","title":"U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection","created":"2020-05-18","cited_by_count":412},{"id":"1812.11788","title":"PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation","created":"2018-12-31","cited_by_count":385},{"id":"1901.04780","title":"DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion","created":"2019-01-15","cited_by_count":355},{"id":"1803.07549","title":"Learning Category-Specific Mesh Reconstruction from Image Collections","created":"2018-03-20","cited_by_count":317},{"id":"1804.00175","title":"DeepIM: Deep Iterative Matching for 6D Pose Estimation","created":"2018-03-31","cited_by_count":283},{"id":"1612.00496","title":"3D Bounding Box Estimation Using Deep Learning and Geometry","created":"2016-12-01","cited_by_count":278},{"id":"1808.08319","title":"BOP: Benchmark for 6D Object Pose Estimation","created":"2018-08-24","cited_by_count":254},{"id":"1711.10006","title":"SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again","created":"2017-11-27","cited_by_count":243},{"id":"1804.04610","title":"Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling","created":"2018-04-12","cited_by_count":218},{"id":"1908.07433","title":"Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation","created":"2019-08-20","cited_by_count":218},{"id":"1711.08848","title":"Real-Time Seamless Single Shot 6D Object Pose Prediction","created":"2017-11-24","cited_by_count":210},{"id":"1704.06254","title":"Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency","created":"2017-04-20","cited_by_count":177},{"id":"2002.12212","title":"Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image","created":"2020-02-27","cited_by_count":172},{"id":"1912.07109","title":"SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization","created":"2019-12-15","cited_by_count":159},{"id":"2012.09988","title":"Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations","created":"2020-12-18","cited_by_count":155},{"id":"1804.00874","title":"CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM","created":"2018-04-03","cited_by_count":152},{"id":"1811.11187","title":"Scan2CAD: Learning CAD Model Alignment in RGB-D Scans","created":"2018-11-27","cited_by_count":142},{"id":"2001.09322","title":"Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation","created":"2020-01-25","cited_by_count":124},{"id":"1711.03129","title":"MarrNet: 3D Shape Reconstruction via 2.5D Sketches","created":"2017-11-08","cited_by_count":108},{"id":"2001.01869","title":"HybridPose: 6D Object Pose Estimation under Hybrid Representations","created":"2020-01-07","cited_by_count":107},{"id":"1612.00814","title":"Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision","created":"2016-12-01","cited_by_count":106},{"id":"1909.00321","title":"Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks","created":"2019-09-01","cited_by_count":106},{"id":"2008.08145","title":"Category Level Object Pose Estimation via Neural Analysis-by-Synthesis","created":"2020-08-18","cited_by_count":100},{"id":"2103.06422","title":"Holistic 3D Scene Understanding from a Single Image with Implicit Representation","created":"2021-03-11","cited_by_count":94},{"id":"1810.09381","title":"Unsupervised Learning of Shape and Pose with Differentiable Point Clouds","created":"2018-10-22","cited_by_count":89},{"id":"1912.00416","title":"LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation","created":"2019-12-01","cited_by_count":89},{"id":"2006.12250","title":"Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images","created":"2020-06-22","cited_by_count":79},{"id":"1608.05137","title":"IM2CAD","created":"2016-08-18","cited_by_count":78},{"id":"1910.10750","title":"6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints","created":"2019-10-23","cited_by_count":78},{"id":"2102.06195","title":"Shelf-Supervised Mesh Prediction in the Wild","created":"2021-02-11","cited_by_count":65},{"id":"1812.11166","title":"Learning to Reconstruct Shapes from Unseen Classes","created":"2018-12-28","cited_by_count":60},{"id":"1411.6069","title":"Category-Specific Object Reconstruction from a Single Image","created":"2014-11-22","cited_by_count":56},{"id":"1801.03910","title":"Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction","created":"2018-01-11","cited_by_count":52},{"id":"1908.00151","title":"Multi-path Learning for Object Pose Estimation Across Domains","created":"2019-08-01","cited_by_count":52},{"id":"2005.05125","title":"FroDO: From Detections to 3D Objects","created":"2020-05-11","cited_by_count":51},{"id":"2004.12989","title":"CoReNet: Coherent 3D scene reconstruction from a single RGB image","created":"2020-04-27","cited_by_count":45},{"id":"1901.06447","title":"Learning single-image 3D reconstruction by generative modelling of shape, pose and shading","created":"2019-01-19","cited_by_count":43},{"id":"2109.00326","title":"Category-Level Metric Scale Object Shape and Pose Estimation","created":"2021-09-01","cited_by_count":41},{"id":"1906.04201","title":"End-to-End CAD Model Retrieval and 9DoF Alignment in 3D Scans","created":"2019-06-10","cited_by_count":40},{"id":"1903.06482","title":"SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations","created":"2019-03-15","cited_by_count":37},{"id":"2003.11089","title":"G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features","created":"2020-03-24","cited_by_count":35},{"id":"1607.06038","title":"Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation","created":"2016-07-20","cited_by_count":34},{"id":"2204.01586","title":"Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image","created":"2022-04-04","cited_by_count":30},{"id":"2007.13124","title":"GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision","created":"2020-07-26","cited_by_count":28},{"id":"2007.13393","title":"Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetry","created":"2020-07-27","cited_by_count":24},{"id":"2001.10609","title":"A Review on Object Pose Recovery: from 3D Bounding Box Detectors to Full 6D Pose Estimators","created":"2020-01-28","cited_by_count":18},{"id":"2005.01939","title":"From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks","created":"2020-05-05","cited_by_count":18},{"id":"1912.10589","title":"Front2Back: Single View 3D Shape Reconstruction via Front to Back Prediction","created":"2019-12-23","cited_by_count":17},{"id":"2005.03717","title":"Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images","created":"2020-05-07","cited_by_count":14},{"id":"1705.10904","title":"Weakly supervised 3D Reconstruction with Adversarial Constraint","created":"2017-05-31","cited_by_count":13},{"id":"1812.01742","title":"Domain-Adaptive Single-View 3D Reconstruction","created":"2018-12-04","cited_by_count":12},{"id":"1907.01085","title":"Multiview Aggregation for Learning Category-Specific Shape Reconstruction","created":"2019-07-01","cited_by_count":12},{"id":"1908.02809","title":"GP2C: Geometric Projection Parameter Consensus for Joint 3D Pose and Focal Length Estimation in the Wild","created":"2019-08-07","cited_by_count":9},{"id":"2003.05848","title":"CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning","created":"2020-03-12","cited_by_count":7},{"id":"2111.05615","title":"Leveraging Geometry for Shape Estimation from a Single RGB Image","created":"2021-11-10","cited_by_count":4},{"id":"1602.01464","title":"Latent-Class Hough Forests for 6 DoF Object Pose Estimation","created":"2016-02-03","cited_by_count":3}],"next_cursor":null}