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[CVPR] GIFT: A Real-time and Scalable 3D Shape Search Engine. [Retrieval.]
[ECCV] Deep learning 3D shape surfaces using geometry images. [Classification.Retrieval.]
[CVPR] Pairwise decomposition of image sequences for active multi-view recognition. [Classification.Retrieval.]
[CVPR] Volumetric and multi-view CNNs for object classification on 3D data. [lua] [Classification.Retrieval.]
[arXiv] FusionNet: 3D object classification using multiple data representations. [Classification.]
2017
[BMVC] Dominant set clustering and pooling for multi-view 3D object recognition. [MATLAB] [Classification.]
[IGTA] Boosting multi-view convolutional neural networks for 3D object recognition via view saliency. [Classification.]
[3DOR] Exploiting the PANORAMA Representation for Convolutional Neural Network Classification and Retrieval. [Classification.Retrieval.]
2018
[IEEE TRANSACTION ON MULTIMEDIA] Learning Multi-view Representation with LSTM for 3D Shape Recognition and Retrieval. [Classification.Retrieval.]
[IEEE Transactions on Image Processing] SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN with Attention. [Classification.Retrieval.]
[CVPR] GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition. [tensorflow] [pytorch][Classification.Retrieval.] 🔥 ⭐
[CVPR] Rotationnet: Joint object categorization and pose estimation using multiviews from unsupervised viewpoints. [tensorflow] [pytorch][Classification.Retrieval.] 🔥 ⭐
[CVPR] Multi-view harmonized bilinear network for 3d object recognition. [pytorch] [Classification.]
2019
[IEEE Transactions on Image Processing] 3D2SeqViews: Aggregating Sequential Views for 3D Global Feature Learning by CNN with Hierarchical Attention Aggregation . [Classification.Retrieval.]
[AAAI] MLVCNN: Multi-Loop-View Convolutional Neural Network for 3D Shape Retrieval. [Classification.Retrieval.]
[AAAI] DeepCCFV: Camera Constraint-Free Multi-View Convolutional Neural Network for 3D Object Retrieval. [Classification.Retrieval.]
[CVPR] Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition. [pytorch] [Classification.Retrieval.]
[AAAI] Angular Triplet-Center Loss for Multi-View 3D Shape Retrieval.[Classification.Retrieval.]
[IJCAI] Rethinking Loss Design for Large-scale 3D Shape Retrieval.[Classification.Retrieval.]
[IJCAI] Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views.[Classification.Retrieval.]
[IJCAI] 3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention.[Classification.Retrieval.]
[AAAI] View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions.[Classification.Retrieval.]
[ICCV] View N-gram Network for 3D Object Retrieval. [Classification.Retrieval.]
2020
[CVPR] View-GCN: View-based Graph Convolutional Network for 3D Shape Analysis. [pytorch][Classification.Retrieval.] 🔥 ⭐
[SemanticKITTI] Sequential Semantic Segmentation, 28 classes, for autonomous driving. All sequences of KITTI odometry labeled. [ICCV 2019 paper]
[The Waymo Open Dataset] The Waymo Open Dataset is comprised of high resolution sensor data collected by Waymo self-driving cars in a wide variety of conditions.
[Oxford Robotcar] The dataset captures many different combinations of weather, traffic and pedestrians.