{"list":"other","total":59,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1612.00593","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","created":"2016-12-02","cited_by_count":5248},{"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":"1801.07829","title":"Dynamic Graph CNN for Learning on Point Clouds","created":"2018-01-24","cited_by_count":2223},{"id":"2011.00931","title":"Point Transformer","created":"2020-11-02","cited_by_count":1470},{"id":"1904.08889","title":"KPConv: Flexible and Deformable Convolution for Point Clouds","created":"2019-04-18","cited_by_count":1205},{"id":"1802.08219","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","created":"2018-02-22","cited_by_count":746},{"id":"1801.09847","title":"Open3D: A Modern Library for 3D Data Processing","created":"2018-01-30","cited_by_count":721},{"id":"1811.07246","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","created":"2018-11-17","cited_by_count":657},{"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":"2303.05511","title":"Scaling up GANs for Text-to-Image Synthesis","created":"2023-03-09","cited_by_count":492},{"id":"2104.12229","title":"Vector Neurons: A General Framework for SO(3)-Equivariant Networks","created":"2021-04-25","cited_by_count":305},{"id":"1711.06721","title":"Learning SO(3) Equivariant Representations with Spherical CNNs","created":"2017-11-17","cited_by_count":248},{"id":"2108.13826","title":"Self-Calibrating Neural Radiance Fields","created":"2021-08-31","cited_by_count":204},{"id":"1807.02443","title":"Tangent Convolutions for Dense Prediction in 3D","created":"2018-07-06","cited_by_count":173},{"id":"2103.14147","title":"Equivariant Point Network for 3D Point Cloud Analysis","created":"2021-03-25","cited_by_count":81},{"id":"2111.11187","title":"PointMixer: MLP-Mixer for Point Cloud Understanding","created":"2021-11-22","cited_by_count":71},{"id":"2109.00182","title":"You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors","created":"2021-09-01","cited_by_count":68},{"id":"2003.07238","title":"A Rotation-Invariant Framework for Deep Point Cloud Analysis","created":"2020-03-16","cited_by_count":62},{"id":"1909.02533","title":"C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion","created":"2019-09-05","cited_by_count":61},{"id":"2004.00542","title":"Future Video Synthesis with Object Motion Prediction","created":"2020-04-01","cited_by_count":57},{"id":"2111.01118","title":"Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training","created":"2021-11-01","cited_by_count":57},{"id":"2109.04310","title":"Deep Hough Voting for Robust Global Registration","created":"2021-09-09","cited_by_count":53},{"id":"2008.02792","title":"CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations","created":"2020-08-06","cited_by_count":51},{"id":"2206.10843","title":"Learning Debiased Classifier with Biased Committee","created":"2022-06-22","cited_by_count":51},{"id":"2111.00190","title":"Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation","created":"2021-10-30","cited_by_count":45},{"id":"1912.12098","title":"Quaternion Equivariant Capsule Networks for 3D Point Clouds","created":"2019-12-27","cited_by_count":41},{"id":"2010.03318","title":"Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud","created":"2020-10-07","cited_by_count":38},{"id":"2206.09479","title":"StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis","created":"2022-06-19","cited_by_count":38},{"id":"2306.07200","title":"Fill-Up: Balancing Long-Tailed Data with Generative Models","created":"2023-06-12","cited_by_count":34},{"id":"2008.09110","title":"Weakly-supervised 3D Shape Completion in the Wild","created":"2020-08-20","cited_by_count":33},{"id":"2203.14486","title":"Equivariant Point Cloud Analysis via Learning Orientations for Message Passing","created":"2022-03-28","cited_by_count":32},{"id":"2111.14562","title":"Instance-wise Occlusion and Depth Orders in Natural Scenes","created":"2021-11-29","cited_by_count":31},{"id":"2201.07788","title":"ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes","created":"2022-01-19","cited_by_count":31},{"id":"1911.09040","title":"3D-Rotation-Equivariant Quaternion Neural Networks","created":"2019-11-20","cited_by_count":30},{"id":"1911.02163","title":"SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentation","created":"2019-11-06","cited_by_count":29},{"id":"2011.03298","title":"Learning to Orient Surfaces by Self-supervised Spherical CNNs","created":"2020-11-06","cited_by_count":26},{"id":"2005.08144","title":"High-dimensional Convolutional Networks for Geometric Pattern Recognition","created":"2020-05-17","cited_by_count":24},{"id":"2011.09080","title":"Deep Positional and Relational Feature Learning for Rotation-Invariant Point Cloud Analysis","created":"2020-11-18","cited_by_count":22},{"id":"2110.15481","title":"Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning","created":"2021-10-29","cited_by_count":19},{"id":"2208.11537","title":"PeRFception: Perception using Radiance Fields","created":"2022-08-24","cited_by_count":19},{"id":"2110.00119","title":"HUMBI: A Large Multiview Dataset of Human Body Expressions and Benchmark Challenge","created":"2021-09-30","cited_by_count":17},{"id":"2204.01159","title":"Shape-Pose Disentanglement using SE(3)-equivariant Vector Neurons","created":"2022-04-03","cited_by_count":16},{"id":"2303.14378","title":"Instant Domain Augmentation for LiDAR Semantic Segmentation","created":"2023-03-25","cited_by_count":16},{"id":"2003.00856","title":"Triangle-Net: Towards Robustness in Point Cloud Learning","created":"2020-02-27","cited_by_count":13},{"id":"2004.07414","title":"Combinatorial 3D Shape Generation via Sequential Assembly","created":"2020-04-16","cited_by_count":13},{"id":"2205.13371","title":"A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning","created":"2022-05-25","cited_by_count":10},{"id":"2108.09000","title":"Deep Virtual Markers for Articulated 3D Shapes","created":"2021-08-20","cited_by_count":7},{"id":"1608.05204","title":"Refining Geometry from Depth Sensors using IR Shading Images","created":"2016-08-18","cited_by_count":5},{"id":"2207.04221","title":"Learning to Register Unbalanced Point Pairs","created":"2022-07-09","cited_by_count":5},{"id":"2111.05980","title":"Self-Supervised Real-time Video Stabilization","created":"2021-11-10","cited_by_count":4},{"id":"2210.01021","title":"Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction","created":"2022-10-03","cited_by_count":4},{"id":"2108.09007","title":"Spatiotemporal Texture Reconstruction for Dynamic Objects Using a Single RGB-D Camera","created":"2021-08-20","cited_by_count":3},{"id":"2205.11809","title":"Learning to Assemble Geometric Shapes","created":"2022-05-24","cited_by_count":3},{"id":"2112.01316","title":"Putting 3D Spatially Sparse Networks on a Diet","created":"2021-12-02","cited_by_count":2},{"id":"2112.02779","title":"Revisiting LiDAR Registration and Reconstruction: A Range Image Perspective","created":"2021-12-06","cited_by_count":2},{"id":"2208.14863","title":"Style-Agnostic Reinforcement Learning","created":"2022-08-31","cited_by_count":2},{"id":"2210.08243","title":"Substructure-Atom Cross Attention for Molecular Representation Learning","created":"2022-10-15","cited_by_count":2},{"id":"2210.12350","title":"Instance-Aware Image Completion","created":"2022-10-22","cited_by_count":1}],"next_cursor":null}