Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction
暂无分享,去创建一个
Andrew Markham | Sen Wang | Niki Trigoni | Bo Yang | A. Markham | Sen Wang | Bo Yang | Niki Trigoni
[1] Jianxiong Xiao,et al. 3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Hongdong Li,et al. “Maximizing Rigidity” Revisited: A Convex Programming Approach for Generic 3D Shape Reconstruction from Multiple Perspective Views , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[3] Mathieu Salzmann,et al. Deep Attentional Structured Representation Learning for Visual Recognition , 2018, BMVC.
[4] Deva Ramanan,et al. Attentional Pooling for Action Recognition , 2017, NIPS.
[5] Andrew Zisserman,et al. Learning to Predict 3D Surfaces of Sculptures from Single and Multiple Views , 2018, International Journal of Computer Vision.
[6] Junsong Yuan,et al. Multi-view Harmonized Bilinear Network for 3D Object Recognition , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[7] Yoshua Bengio,et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.
[8] Han Zhang,et al. Self-Attention Generative Adversarial Networks , 2018, ICML.
[9] Hao Su,et al. A Point Set Generation Network for 3D Object Reconstruction from a Single Image , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Ronen Basri,et al. A Survey on Structure from Motion , 2017, ArXiv.
[11] Samy Bengio,et al. Order Matters: Sequence to sequence for sets , 2015, ICLR.
[12] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[13] Yoshua Bengio,et al. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .
[14] Alexander J. Smola,et al. Stacked Attention Networks for Image Question Answering , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Koray Kavukcuoglu,et al. Neural scene representation and rendering , 2018, Science.
[16] Daniel P. W. Ellis,et al. Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems , 2015, ArXiv.
[17] G LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[18] Alexander J. Smola,et al. Deep Sets , 2017, 1703.06114.
[19] Horst Bischof,et al. OctNetFusion: Learning Depth Fusion from Data , 2017, 2017 International Conference on 3D Vision (3DV).
[20] Subhransu Maji,et al. Bilinear CNN Models for Fine-Grained Visual Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[21] Pengfei Xiong,et al. Pyramid Attention Network for Semantic Segmentation , 2018, BMVC.
[22] Marc Levoy,et al. A volumetric method for building complex models from range images , 1996, SIGGRAPH.
[23] Hongbin Zha,et al. PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction , 2018, ECCV.
[24] Subhransu Maji,et al. Second-order Democratic Aggregation , 2018, ECCV.
[25] Mathieu Salzmann,et al. Statistically Motivated Second Order Pooling , 2018, ECCV.
[26] Max Welling,et al. Attention-based Deep Multiple Instance Learning , 2018, ICML.
[27] Shi-Min Hu,et al. Learning to Reconstruct High-Quality 3D Shapes with Cascaded Fully Convolutional Networks , 2018, ECCV.
[28] Subhransu Maji,et al. Multi-view Convolutional Neural Networks for 3D Shape Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[29] John J. Leonard,et al. Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age , 2016, IEEE Transactions on Robotics.
[30] Jordi Gonzàlez,et al. Attend and Rectify: a Gated Attention Mechanism for Fine-Grained Recovery , 2018, ECCV.
[31] Matthijs C. Dorst. Distinctive Image Features from Scale-Invariant Keypoints , 2011 .
[32] Luc Van Gool,et al. RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[33] Leonidas J. Guibas,et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Jiong Wang,et al. Attention-based Pyramid Aggregation Network for Visual Place Recognition , 2018, ACM Multimedia.
[35] Leonidas J. Guibas,et al. ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.
[36] Andrew W. Fitzgibbon,et al. Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.
[37] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[38] Ioannis A. Kakadiaris,et al. Deep Imbalanced Attribute Classification using Visual Attention Aggregation , 2018, ECCV.
[39] Gang Sun,et al. Squeeze-and-Excitation Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[40] Eric Martin,et al. Parallelizing Linear Recurrent Neural Nets Over Sequence Length , 2017, ICLR.
[41] Hongdong Li,et al. Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[42] Chao Yang,et al. Dependency-Aware Attention Control for Unconstrained Face Recognition with Image Sets , 2018, ECCV.
[43] Andrew Zisserman,et al. SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes , 2017, BMVC.
[44] Subhransu Maji,et al. Improved Bilinear Pooling with CNNs , 2017, BMVC.
[45] Long Quan,et al. MVSNet: Depth Inference for Unstructured Multi-view Stereo , 2018, ECCV.
[46] Jitendra Malik,et al. Learning a Multi-View Stereo Machine , 2017, NIPS.
[47] R. Mesiar,et al. Aggregation operators: properties, classes and construction methods , 2002 .
[48] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Silvio Savarese,et al. 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction , 2016, ECCV.
[50] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.
[51] Xin Yang,et al. Active Object Reconstruction Using a Guided View Planner , 2018, IJCAI.
[52] Bernhard P. Wrobel,et al. Multiple View Geometry in Computer Vision , 2001 .
[53] Lu Fang,et al. SurfaceNet: An End-to-End 3D Neural Network for Multiview Stereopsis , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[54] Leonidas J. Guibas,et al. Volumetric and Multi-view CNNs for Object Classification on 3D Data , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[55] Rastko R. Selmic,et al. Classifying Unordered Feature Sets with Convolutional Deep Averaging Networks , 2017, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).
[56] Thomas Brox,et al. Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[57] Narendra Ahuja,et al. DeepMVS: Learning Multi-view Stereopsis , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[58] Cristian Sminchisescu,et al. Matrix Backpropagation for Deep Networks with Structured Layers , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).